In typical Web-industry style we’re all focused minutely on the leading trend-of-the-year, the real-time Web. But in this obsession we have become a bit myopic. The real-time Web, or what some of us call “The Stream,” is not an end in itself, it’s a means to an end. So what will it enable, where is it headed, and what’s it going to look like when we look back at this trend in 10 or 20 years?

In the next 10 years, The Stream is going to go through two big phases, focused on two problems, as it evolves:

1. Web Attention Deficit Disorder. The first problem with the real-time Web that is becoming increasingly evident is that it has a bad case of ADD. There is so much information streaming in from so many places at once that it’s simply impossible to focus on anything for very long, and a lot of important things are missed in the chaos. The first generation of tools for the Stream are going to need to address this problem.
2. Web Intention Deficit Disorder. The second problem with the real-time Web will emerge after we have made some real headway in solving Web attention deficit disorder. This second problem is about how to get large numbers of people to focus their intention not just their attention. It’s not just difficult to get people to notice something, it’s even more difficult to get them to do something. Attending to something is simply noticing it. Intending to do something is actually taking action, expending some energy or effort to do something. Intending is a lot more expensive, cognitively speaking, than merely attending. The power of collective intention is literally what changes the world, but we don’t have the tools to direct it yet.

The Stream is not the only big trend taking place right now. In fact, it’s just a strand that is being braided together with several other trends, as part of a larger pattern. Here are some of the other strands I’m tracking:

• Messaging. The real-time Web aka The Stream is really about messaging in essence. It’s a subset of the global trend towards building a better messaging layer for the Web. Multiple forms of messaging are emerging, from the publish-and-subscribe nature of Twitter and RSS, to things like Google Wave, Pubsubhubub, and broadcast style messaging or multicasting via screencast, conferencing and media streaming and events in virtual worlds. The effect of these tools is that the speed and interactivity of the Web are increasing — the Web is getting faster. Information spreads more virally, more rapidly — in other words, “memes” (which we can think of as collective thoughts) are getting more sophisticated and gaining more mobility.
• Semantics. The Web becomes more like a database. The resolution of search, ad targeting, and publishing increases. In other words, it’s a higher-resolution Web. Search will be able to target not just keywords but specific meaning. For example, you will be able to search precisely for products or content that meet certain constraints. Multiple approaches from natural language search to the metadata of the Semantic Web will contribute to increased semantic understanding and representation of the Web.
• Attenuation. As information moves faster, and our networks get broader, information overload gets worse in multiple dimensions. This creates a need for tools to help people filter the firehose. Filtering in its essence is a process of attenuation — a way to focus attention more efficiently on signal versus noise. Broadly speaking there are many forms of filtering from automated filtering, to social filtering, to personalization, but they all come down to helping someone focus their finite attention more efficiently on the things they care about most.
• The WebOS.  As cloud computing resources, mashups, open linked data, and open API’s proliferate, a new level of aggregator is emerging. These aggregators may focus on one of these areas or may cut across them. Ultimately they are the beginning of true cross-service WebOS’s. I predict this is going to be a big trend in the future — for example instead of writing Web apps directly to various data and API’s in dozens of places, just write to a single WebOS aggregator that acts as middleware between your app and all these choices. It’s much less complicated for developers. The winning WebOS is probably not going to come from Google, Microsoft or Amazon — rather it will probably come from someone neutral, with the best interests of developers as the primary goal.
• Decentralization. As the semantics of the Web get richer, and the WebOS really emerges it will finally be possible for applications to leverage federated, Web-scale computing. This is when intelligent agents will actually emerge and be practical. By this time the Web will be far too vast and complex and rapidly changing for any centralized system to index and search it. Only massively federated swarms of intelligent agents, or extremely dynamic distributed computing tools, that can spread around the Web as they work, will be able to keep up with the Web.
• Socialization. Our interactions and activities on the Web are increasingly socially networked, whether individual, group or involving large networks or crowds. Content is both shared and discovered socially through our circles of friends and contacts. In addition, new technologies like Google Social Search enable search results to be filtered by social distance or social relevancy. In other words, things that people you follow like get higher visibility in your search results. Socialization is a trend towards making previously non-social activities more social, and towards making already-social activities more efficient and broader. Ultimately this process leads to wider collaboration and higher levels of collective intelligence.
• Augmentation. Increasingly we will see a trend towards augmenting things with other things. For example, augmenting a Web page or data set with links or notes from another Web page or data set. Or augmenting reality by superimposing video and data onto a live video image on a mobile phone. Or augmenting our bodies with direct connections to computers and the Web.

If these are all strands in a larger pattern, then what is the megatrend they are all contributing to? I think ultimately it’s collective intelligence — not just of humans, but also our computing systems, working in concert.

Collective Intelligence

I think that these trends are all combining, and going real-time. Effectively what we’re seeing is the evolution of a global collective mind, a theme I keep coming back to again and again. This collective mind is not just comprised of humans, but also of software and computers and information, all interlinked into one unimaginably complex system: A system that senses the universe and itself, that thinks, feels, and does things, on a planetary scale. And as humanity spreads out around the solar system and eventually the galaxy, this system will spread as well, and at times splinter and reproduce.

But that’s in the very distant future still. In the nearer term — the next 100 years or so — we’re going to go through some enormous changes. As the world becomes increasingly networked and social the way collective thinking and decision making take place is going to be radically restructured.

Social Evolution

Existing and established social, political and economic structures are going to either evolve or be overturned and replaced. Everything from the way news and entertainment are created and consumed, to how companies, cities and governments are managed will change radically. Top-down beaurocratic control systems are simply not going to be able to keep up or function effectively in this new world of distributed, omnidirectional collective intelligence.

Physical Evolution

As humanity and our Web of information and computatoins begins to function as a single organism, we will evolve literally, into a new species: Whatever is after the homo sapien. The environment we will live in will be a constantly changing sea of collective thought in which nothing and nobody will be isolated. We will be more interdependent than ever before. Interdependence leads to symbiosis, and eventually to the loss of generality and increasing specialization. As each of us is able to draw on the collective mind, the global brain, there may be less pressure on us to do things on our own that used to be solitary. What changes to our bodies, minds and organizations may result from these selective evolutionary pressures? I think we’ll see several, over multi-thousand year timescales, or perhaps faster if we start to genetically engineer ourselves:

• Individual brains will get less good at things like memorization and recall, calculation, reasoning, and long-term planning and action.
• Individual brains will get better at multi-tasking, information filtering, trend detection, and social communication. The parts of the nervous system involved in processing live information will increase disproportionately to other parts.
• Our bodies may actually improve in certain areas. We will become more, not less, mobile, as computation and the Web become increasingly embedded into our surroundings, and into augmented views of our environments. This may cause our bodies to get into better health and shape since we will be less sedentary, less at our desks, less in front of TV’s. We’ll be moving around in the world, connected to everything and everyone no matter where we are. Physical strength will probably decrease overall as we will need to do less manual labor of any kind.

These are just some of the changes that are likely to occur as a result of the things we’re working on today. The Web and the emerging Real-Time Web are just a prelude of things to come.

The next generation of Web search is coming sooner than expected. And with it we will see several shifts in the way people search, and the way major search engines provide search functionality to consumers.

Web 1.0, the first decade of the Web (1989 – 1999), was characterized by a distinctly desktop-like search paradigm. The overriding idea was that the Web is a collection of documents, not unlike the folder tree on the desktop, that must be searched and ranked hierarchically. Relevancy was considered to be how closely a document matched a given query string.

Web 2.0, the second decade of the Web (1999 – 2009), ushered in the beginnings of a shift towards social search. In particular blogging tools, social bookmarking tools, social networks, social media sites, and microblogging services began to organize the Web around people and their relationships. This added the beginnings of a primitive “web of trust” to the search repertoire, enabling search engines to begin to take the social value of content (as evidences by discussions, ratings, sharing, linking, referrals, etc.) as an additional measurment in the relevancy equation. Those items which were both most relevant on a keyword level, and most relevant in the social graph (closer and/or more popular in the graph), were considered to be more relevant. Thus results could be ranked according to their social value — how many people in the community liked them and current activity level — as
well as by semantic relevancy measures.

In the coming third decade of the Web, Web 3.0 (2009 – 2019), there will be another shift in the search paradigm. This is a shift to from the past to the present, and from the social to the personal.

Established search engines like Google rank results primarily by keyword (semantic) relevancy. Social search engines rank results primarily by activity and social value (Digg, Twine 1.0, etc.). But the new search engines of the Web 3.0 era will also take into account two additional factors when determining relevancy: timeliness, and personalization.

Google returns the same results for everyone. But why should that be the case? In fact, when two different people search for the same information, they may want to get very different kinds of results. Someone who is a novice in a field may want beginner-level information to rank higher in the results than someone who is an expert. There may be a desire to emphasize things that are novel over things that have been seen before, or that have happened in the past — the more timely something is the more relevant it may be as well.

These two themes — present and personal — will define the next great search experience.

To accomplish this, we need to make progress on a number of fronts.

First of all, search engines need better ways to understand what content is, without having to do extensive computation. The best solution for this is to utilize metadata and the methods of the emerging semantic web.

Metadata reduces the need for computation in order to determine what content is about — it makes that explicit and machine-understandable. To the extent that machine-understandable metadata is added or generated for the Web, it will become more precisely searchable and productive for searchers.

This applies especially to the area of the real-time Web, where for example short “tweets” of content contain very little context to support good natural-language processing. There a little metadata can go a long way. In addition, of course metadata makes a dramatic difference in search of the larger non-real-time Web as well.

In addition to metadata, search engines need to modify their algorithms to be more personalized. Instead of a “one-size fits all” ranking for each query, the ranking may differ for different people depending on their varying interests and search histories.

Finally, to provide better search of the present, search has to become more realtime. To this end, rankings need to be developed that surface not only what just happened now, but what happened recently and is also trending upwards and/or of note. Realtime search has to be more than merely listing search results chronologically. There must be effective ways to filter the noise and surface what’s most important effectively. Social graph analysis is a key tool for doing this, but in
addition, powerful statistical analysis and new visualizations may also be required to make a compelling experience.

Sneak Preview of Siri – The Virtual Assistant that will Make Everyone Love the iPhone, Part 2: The Technical Stuff

In Part-One of this article on TechCrunch, I covered the emerging paradigm of Virtual Assistants and explored a first look at a new product in this category called Siri. In this article, Part-Two, I interview Tom Gruber, CTO of Siri, about the history, key ideas, and technical foundations of the product:

Nova Spivack: Can you give me a more precise definition of a Virtual Assistant?

Tom Gruber: A virtual personal assistant is a software system that

• Helps the user find or do something (focus on tasks, rather than information)
• Understands the user’s intent (interpreting language) and context (location, schedule, history)
• Works on the user’s behalf, orchestrating multiple services and information sources to help complete the task

In other words, an assistant helps me do things by understanding me and working for me. This may seem quite general, but it is a fundamental shift from the way the Internet works today. Portals, search engines, and web sites are helpful but they don’t do things for me – I have to use them as tools to do something, and I have to adapt to their ways of taking input.

Nova Spivack: Siri is hoping to kick-start the revival of the Virtual Assistant category, for the Web. This is an idea which has a rich history. What are some of the past examples that have influenced your thinking?

Tom Gruber: The idea of interacting with a computer via a conversational interface with an assistant has excited the imagination for some time.  Apple’s famous Knowledge Navigator video offered a compelling vision, in which a talking head agent helped a professional deal with schedules and access information on the net. The late Michael Dertouzos, head of MIT’s Computer Science Lab, wrote convincingly about the assistant metaphor as the natural way to interact with computers in his book “The Unfinished Revolution: Human-Centered Computers and What They Can Do For Us”.  These accounts of the future say that you should be able to talk to your computer in your own words, saying what you want to do, with the computer talking back to ask clarifying questions and explain results.  These are hallmarks of the Siri assistant.  Some of the elements of these visions
are beyond what Siri does, such as general reasoning about science in the Knowledge Navigator.  Or self-awareness a la Singularity.  But Siri is the real thing, using real AI technology, just made very practical on a small set of domains. The breakthrough is to bring this vision to a mainstream market, taking maximum advantage of the mobile context and internet service ecosystems.

Nova Spivack: Tell me about the CALO project, that Siri spun out from. (Disclosure: my company, Radar Networks, consulted to SRI in the early days on the CALO project, to provide assistance with Semantic Web development)

Tom Gruber: Siri has its roots in the DARPA CALO project (“Cognitive Agent that Learns and Organizes”) which was led by SRI. The goal of CALO was to develop AI technologies (dialog and natural language understanding,s understanding, machine learning, evidential and probabilistic reasoning, ontology and knowledge representation, planning, reasoning, service delegation) all integrated into a virtual
assistant that helps people do things.  It pushed the limits on machine learning and speech, and also showed the technical feasibility of a task-focused virtual assistant that uses knowledge of user context and multiple sources to help solve problems.

Siri is integrating, commercializing, scaling, and applying these technologies to a consumer-focused virtual assistant.  Siri was under development for several years during and after the CALO project at SRI. It was designed as an independent architecture, tightly integrating the best ideas from CALO but free of the constraints of a national distributed research project. The Siri.com team has been evolving and hardening the technology since January 2008.

Nova Spivack: What are primary aspects of Siri that you would say are “novel”?

Tom Gruber: The demands of the consumer internet focus — instant usability and robust interaction with the evolving web — has driven us to come up with some new innovations:

• A conversational interface that combines the best of speech and semantic language understanding with an interactive dialog that helps guide
  people toward saying what they want to do and getting it done. The
  conversational interface allows for much more interactivity that one-shot search style interfaces, which aids usability and improves intent understanding.  For example, if Siri didn’t quite hear what you said, or isn’t sure what you meant, it can ask for clarifying information.   For example, it can prompt on ambiguity: did you mean pizza restaurants in Chicago or Chicago-style pizza places near you? It can also make reasonable guesses based on context. Walking around with the phone at lunchtime, if the speech interpretation comes back with something garbled about food you probably meant “places to eat near my current location”. If this assumption isn’t right, it is easy to correct in a conversation.
• Semantic auto-complete – a combination of the familiar “autocomplete” interface of search boxes with a semantic and linguistic model of what might be worth saying. The so-called “semantic completion” makes it possible to rapidly state complex requests (Italian restaurants in the SOMA neighborhood of San Francisco that have tables available tonight) with just a few clicks. It’s sort of like the power of faceted search a la Kayak, but packaged in a clever command line style interface that works in small form factor and low bandwidth environments.
• Service delegation – Siri is particularly deep in technology for operationalizing a user’s intent into computational form, dispatching to multiple, heterogeneous services, gathering and integrating results, and presenting them back to the user as a set of solutions to their request.  In a restaurant selection task, for instance, Siri combines information from many different sources (local business directories, geospatial databases, restaurant guides, restaurant review sources, online reservation services, and the user’s own favorites) to show a set of candidates that meet the intent expressed in the user’s natural language request.

Nova Spivack: Why do you think Siri will succeed when other AI-inspired projects have failed to meet expectations?

Tom Gruber: In general my answer is that Siri is more focused. We can break this down into three areas of focus:

• Task focus. Siri is very focused on a bounded set of specific human tasks, like finding something to do, going out with friends, and getting around town.  This task focus allows it to have a very rich model of its domain of competence, which makes everything more tractable from language understanding to reasoning to service invocation and results presentation
  
• Structured data focus. The kinds of tasks that Siri is particularly good at involve semistructured data, usually on tasks involving multiple criteria and drawing from multiple sources.  For example, to help find a place to eat, user preferences for cuisine, price range, location, or even specific food items come into play.  Combining results from multiple sources requires
  reasoning about domain entity identity and the relative capabilities of different information providers.  These are hard problems of semantic
  information processing and integration that are difficult but feasible
  today using the latest AI technologies.
• Architecture focus. Siri is built from deep experience in integrating multiple advanced technologies into a platform designed expressly for virtual assistants. Siri co-founder Adam Cheyer was chief architect of the CALO project, and has applied a career of experience to design the platform of the Siri product. Leading the CALO project taught him a lot about what works and doesn’t when applying AI to build a virtual assistant. Adam and I also have rather unique experience in combining AI with intelligent interfaces and web-scale knowledge integration. The result is a “pure  play” dedicated architecture for virtual assistants, integrating all the components of intent understanding, service delegation, and dialog flow management. We have avoided the need to solve general AI problems by concentrating on only what is needed for a virtual assistant, and have chosen to begin with a
  finite set of vertical domains serving mobile use cases.

Nova Spivack: Why did you design Siri primarily for mobile devices, rather than Web browsers in general?

Tom Gruber: Rather than trying to be like a search engine to all the world’s information, Siri is going after mobile use cases where deep models of context (place, time, personal history) and limited form factors magnify the power of an intelligent interface.  The smaller the form factor, the more mobile the context,
the more limited the bandwidth : the more it is important that the interface make intelligent use of the user’s attention and the resources at hand.  In other words, “smaller needs to be smarter.”  And the benefits of being offered just the right level of detail or being prompted with just the right questions can make the difference between task completion or failure.  When you are on the go, you just don’t have time to wade through pages of links and disjoint interfaces, many of which are not suitable to mobile at all.

Nova Spivack: What language and platform is Siri written in?

Tom Gruber: Java, Javascript, and Objective C (for the iPhone)

Nova Spivack: What about the Semantic Web? Is Siri built with Semantic Web open-standards such as RDF and OWL, Sparql?

Tom Gruber: No, we connect to partners on the web using structured APIs, some of which do use the Semantic Web standards.  A site that exposes RDF usually has an API that is easy to deal with, which makes our life easier.  For instance, we use geonames.org as one of our geospatial information sources. It is a full-on Semantic
Web endpoint, and that makes it easy to deal with.  The more the API declares its data model, the more automated we can make our coupling to it.

Nova Spivack: Siri seems smart, at least about the kinds of tasks it was designed for. How is the knowledge represented in Siri – is it an ontology or something else?

Tom Gruber: Siri’s knowledge is represented in a unified modeling system that combines ontologies, inference networks, pattern matching agents, dictionaries, and dialog models.  As much as possible we represent things declaratively (i.e., as data in models, not lines of code).  This is a tried and true best practice for complex AI systems.  This makes the whole system more robust and scalable, and the development process more agile.  It also helps with reasoning and learning, since Siri can look at what it knows and think about similarities and generalizations at a semantic level.

Nova Spivack: Will Siri be part of the Semantic Web, or at least the open linked data Web (by making open API’s, sharing of linked data, RDF, available, etc.)?

Tom Gruber: Siri isn’t a source of data, so it doesn’t expose data using Semantic Web standards.  In the Semantic Web ecosystem, it is doing something like the vision of a semantic desktop – an intelligent interface that knows about user needs
and sources of information to meet those needs, and intermediates.  The original Semantic Web article in Scientific American included use cases that an assistant would do (check calendars, look for things based on multiple structured criteria, route planning, etc.).  The Semantic Web vision focused on exposing the structured data, but it assumes APIs that can do transactions on the data.  For example, if a virtual assistant wants to schedule a dinner it needs more than the information
about the free/busy schedules of participants, it needs API access to their calendars with appropriate credentials, ways of communicating with the participants via APIs to their email/sms/phone, and so forth. Siri is building on the ecosystem of APIs, which are better if they declare the meaning of the data in and out via ontologies.  That is the original purpose of ontologies-as-specification that I promoted in the
1990s – to help specify how to interact with these agents via knowledge-level APIs.

Siri does, however, benefit greatly from standards for talking about space and time, identity (of people, places, and things), and authentication.  As I called for in my Semantic Web talk in 2007, there is no reason we should be string matching on city names, business names, user names, etc.

All players near the user in the ecommerce value chain get better when the information that the users need can be unambiguously identified, compared, and combined. Legitimate service providers on the supply end of the value chain also benefit, because structured data is harder to scam than text.  So if some service provider offers a multi-criteria decision making service, say, to help make a product purchase in some domain, it is much easier to do fraud detection when the product instances, features, prices, and transaction availability information are all structured data.

Nova Spivack: Siri appears to be able to handle requests in natural language. How good is the natural language processing (NLP) behind it? How have you made it better than other NLP?

Tom Gruber: Siri’s top line measure of success is task completion (not relevance).  A subtask is intent recognition, and subtask of that is NLP.  Speech is another element, which couples to NLP and adds its own issues.  In this context, Siri’s NLP is “pretty darn good” — if the user is talking about something in Siri’s domains of competence, its intent understanding is right the vast majority of the time, even in the face of noise from speech, single finger typing, and bad habits from too much keywordese.  All NLP is tuned for some class of natural language, and Siri’s is tuned for things that people might want to say when talking to a virtual assistant on their phone. We evaluate against a corpus, but I don’tknow how it would compare to standard message and news corpuses using by the NLP research community.

Nova Spivack: Did you develop your own speech interface, or are you using third-party system for that? How good is it? Is it battle-tested?

Tom Gruber: We use third party speech systems, and are architected so we can swap them out and experiment. The one we are currently using has millions of users and continuously updates its models based on usage.

Nova Spivack: Will Siri be able to talk back to users at any point?

Tom Gruber: It could use speech synthesis for output, for the appropriate contexts.  I have a long standing interest in this, as my early graduate work was in communication prosthesis. In the current mobile internet world, however, iPhone-sized screens and 3G networks make it possible to do so more much than read menu items over the phone.  For the blind, embedded appliances, and other applications it would make sense to give Siri voice output.

Nova Spivack: Can you give me more examples of how the NLP in Siri works?

Tom Gruber: Sure, here’s an example, published in the Technology Review, that illustrates what’s going on in a typical dialogue with Siri. (Click link to view the table)

Nova Spivack: How personalized does Siri get – will it recommend different things to me depending on where I am when I ask, and/or what I’ve done in the past? Does it learn?

Tom Gruber: Siri does learn in simple ways today, and it will get more sophisticated with time.  As you said, Siri is already personalized based on immediate context, conversational history, and personal information such as where you live.  Siri doesn’t forget things from request to request, as do stateless systems like search engines. It always considers the user model along with the domain and task models when coming up with results.  The evolution in learning comes as users have a history with Siri, which gives it achance to make some generalizations about preferences.  There is a natural progression with virtual assistants from doing exactly what they are asked, to making recommendations based on assumptions about intent and preference. That is the curve we will explore with experience.

Nova Spivack: How does Siri know what is in various external services – are you mining and doing extraction on their data, or is it all just real-time API calls?

Tom Gruber: For its current domains Siri uses dozens of APIs, and connects to them in both realtime access and batch data synchronization modes.  Siri knows about the data because we (humans) explicitly model what is in those sources.  With declarative representations of data and API capabilities, Siri can reason about the various capabilities of its sources at run time to figure out which combination would best serve the current user request.  For sources that do not have nice APIs or expose data using standards like the Semantic Web, we can draw on a value chain of players that do extract structure by data mining and exposing APIs via scraping.

Nova Spivack: Thank you for the information, Siri might actually make me like the iPhone enough to start using one again.

Tom Gruber: Thank you, Nova, it’s a pleasure to discuss this with someone who really gets the technology and larger issues. I hope Siri does get you to use that iPhone again. But remember, Siri is just starting out and will sometimes say silly things. It’s easy to project intelligence onto an assistant, but Siri isn’t going to pass the Turing Test. It’s just a simpler, smarter way to do what you already want to do. It will be interesting to see how this space evolves, how people will come to understand what to expect from the little personal assistant in their pocket.

I’ve written a new article about how content distribution has evolved, and where it is heading. It’s published here: http://www.siliconangle.com/social-media/content-distribution-is-changing-again/.

If you are interested in semantics, taxonomies, education, information overload and how libraries are evolving, you may enjoy this video of my talk on the Semantic Web and the Future of Libraries at the OCLC Symposium at the American Library Association Midwinter 2009 Conference. This event focused around a dialogue between David Weinberger and myself, moderated by Roy Tennant. We were forutnate to have an audience of about 500 very vocal library directors in the audience and it was an intensive day of thinking together. Thanks to the folks at OCLC for a terrific and really engaging event!

Twine has been growing at 50% per month since launch in October. We've been keeping that quiet while we wait to see if it holds. VentureBeat just noticed and did an article about it. It turns out our January numbers are higher than Compete.com estimates and February is looking strong too. We have a slew of cool viral features coming out in the next few months too as we start to integrate with other social networks. Should be an interesting season.

In this interview with Fast Company, I discuss my concept of "connective intelligence." Intelligence is really in the connections between things, not the things themselves. Twine facilitates smarter connections between content, and between people. This facilitates the emergence of higher levels of collective intelligence.

UPDATE: There’s already a lot of good discussion going on around this post in my public twine.

I’ve been writing about a new trend that I call “interest networking” for a while now. But I wanted to take the opportunity before the public launch of Twine on Tuesday (tomorrow) to reflect on the state of this new category of applications, which I think is quickly reaching its tipping point. The concept is starting to catch on as people reach for more depth around their online interactions.

In fact – that’s the ultimate value proposition of interest networks – they move us beyond the super poke and towards something more meaningful. In the long-term view, interest networks are about building a global knowledge commons. But in the short term, the difference between social networks and interest networks is a lot like the difference between fast food and a home-cooked meal – interest networks are all about substance.

At a time when social media fatigue is setting in, the news cycle is growing shorter and shorter, and the world is delivered to us in soundbytes and catchphrases, we crave substance. We go to great lengths in pursuit of substance. Interest networks solve this problem – they deliver substance.t

So, what is an interest network?

In short, if a social network is about who you are interested in, an interest network is about what you are interested in. It’s the logical next step.

Twine for example, is an interest network that helps you share information with friends, family, colleagues and groups, based on mutual interests. Individual “twines” are created for content around specific subjects. This content might include bookmarks, videos, photos, articles, e-mails, notes or even documents. Twines may be public or private and can serve individuals, small groups or even very large groups of members.

I have also written quite a bit about the Semantic Web and the Semantic Graph, and Tim Berners-Lee has recently started talking about what he calls the GGG (Giant Global Graph). Tim and I are in agreement that social networks merely articulate the relationships between people. Social networks do not surface the equally, if not more important, relationships between people and places, places and organizations, places and other places, organization and other organizations, organization and events, documents and documents, and so on.

This is where interest networks come in. It’s still early days to be clear, but interest networks are operating on the premise of tapping into a multi–dimensional graph that manifests the complexity and substance of our world, and delivers the best of that world to you, every day.

We’re seeing more and more companies think about how to capitalize on this trend. There are suddenly (it seems, but this category has been building for many months) lots of different services that can be viewed as interest networks in one way or another, and here are some examples:

• Twine (my site)
• Friendfeed
• Strands
• Intuu
• SocialMedian
• Zimesh
• Popego
• YourVersion

What all of these interest networks have in common is some sort of a bottom-up, user-driven crawl of the Web, which is the way that I’ve described Twine when we get the question about how we propose to index the entire Web (the answer: we don’t.

We let our users tell us what they’re most interested in, and we follow their lead).

Most interest networks exhibit the following characteristics as well:

• They have some sort of bookmarking/submission/markup function to store and map data (often using existing metaphors, even if what’s under the hood is new)
• They also have some sort of social sharing function to provide the network benefit (this isn’t exclusive to interest networks, obviously, but it is characteristic)
• And in most cases, interest networks look to add some sort of “smarts” or “recommendations” capability to the mix (that is, you get more out than you put in)

This last bullet point is where I see next-generation interest networks really providing the most benefit over social bookmarking tools, wikis, collaboration suites and pure social networks of one kind or another.

To that end, we think that Twine is the first of a new breed of intelligent applications that really get to know you better and better over time – and that the more you use Twine, the more useful it will become. Adding your content to Twine is an investment in the future of your data, and in the future of your interests.

At first Twine begins to enrich your data with semantic tags and links to related content via our recommendations engine that learns over time. Twine also crawls any links it sees in your content and gathers related content for you automatically – adding it to your personal or group search engine for you, and further fleshing out the semantic graph of your interests which in turn results in even more relevant recommendations.

The point here is that adding content to Twine, or other next-generation interest networks, should result in increasing returns. That’s a key characteristic, in fact, of the interest networks of the future – the idea that the ratio of work (input) to utility (output) has no established ceiling.

Another key characteristic of interest networks may be in how they monetize. Instead of being advertising-driven, I think they will focus more on a marketing paradigm. They will be to marketing what search engines were to advertising. For example, Twine will be monetizing our rich model of individual and group interests, using our recommendation engine. When we roll this capability out in 2009, we will deliver extremely relevant, useful content, products and offers directly to users who have demonstrated they are really interested in such information, according to their established and ongoing preferences.

6 months ago, you could not really prove that “interest networking” was a trend, and certainly it wasn’t a clearly defined space. It was just an idea, and a goal. But like I said, I think that we’re at a tipping point, where the technology is getting to a point at which we can deliver greater substance to the user, and where the culture is starting to crave exactly this kind of service as a way of making the Web meaningful again.

I think that interest networks are a huge market opportunity for many startups thinking about what the future of the Web will be like, and I think that we’ll start to see the term used more and more widely. We may even start to see some attention from analysts — Carla, Jeremiah, and others, are you listening?

Now, I obviously think that Twine is THE interest network of choice. After all we helped to define the category, and we’re using the Semantic Web to do it. There’s a lot of potential in our engine and our application, and the growing community of passionate users we’ve attracted.

Our 1.0 release really focuses on UE/usability, which was a huge goal for us based on user feedback from our private beta, which began in March of this year. I’ll do another post soon talking about what’s new in Twine. But our TOS (time on site) at 6 minutes/user (all time) and 12 minutes/user (over the last month) is something that the team here is most proud of – it tells us that Twine is sticky, and that “the dogs are eating the dog food.”

Now that anyone can join, it will be fun and gratifying to watch Twine grow.

Still, there is a lot more to come, and in 2009 our focus is going to shift back to extending our Semantic Web platform and turning on more of the next-generation intelligence that we’ve been building along the way. We’re going to take interest networking to a whole new level.

Stay tuned!

I’ve posted a link to a video of my best talk — given at the GRID ’08 Conference in Stockholm this summer. It’s about the growth of collective intelligence and the Semantic Web, and the future and role the media. Read more and get the video here. Enjoy!

Video from my panel at DEMO Fall ’08 on the Future of the Web is now available.

I moderated the panel, and our panelists were:

Howard Bloom, Author, The Evolution of Mass Mind from the Big Bang to the 21st Century

Peter Norvig, Director of Research, Google Inc.

Jon Udell, Evangelist, Microsoft Corporation

Prabhakar Raghavan, PhD, Head of Research and Search Strategy, Yahoo! Inc.

The panel was excellent, with many DEMO attendees saying it was the best panel they had ever seen at DEMO.

Many new and revealing insights were provided by our excellent panelists. I was particularly interested in the different ways that Google and Yahoo describe what they are working on. They covered lots of new and interesting information about their thinking. Howard Bloom added fascinating comments about the big picture and John Udell helped to speak about Microsoft’s longer-term views as well.

Enjoy!!!

This is an older version of this article. The most recent version is located here:

http://www.readwriteweb.com/archives/future_of_the_desktop.php

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I have spent the last year really thinking about the future of the Web. But lately I have been thinking more about the future of the desktop. In particular, here are some questions I am thinking about and some answers I’ve come up so far.

(Author’s Note: This is a raw, first-draft of what I think it will be like. Please forgive any typos — I am still working on this and editing it…)

What Will Happen to the Desktop?

As we enter the third decade of the Web we are seeing an increasing shift from local desktop applications towards Web-hosted software-as-a-service (SaaS). The full range of standard desktop office tools (word processors, spreadsheets, presentation tools, databases, project management, drawing tools, and more) can now be accessed as Web-hosted apps within the browser. The same is true for an increasing range of enterprise applications. This process seems to be accelerating.

As more kinds of applications become available in Web-based form, the Web browser is becoming the primary framework in which end-users work and interact. But what will happen to the desktop? Will it too eventually become a Web-hosted application? Will the Web browser swallow up the desktop? Where is the desktop headed?

Is the desktop of the future going to just be a web-hosted version of the same old-fashioned desktop metaphors we have today?

No. There have already been several attempts at doing this — and they never catch on. People don’t want to manage all their information on the Web in the same interface they use to manage data and apps on their local PC.

Partly this is due to the difference in user experience between using files and folders on a local machine and doing that in “simulated” fashion via some Flash-based or HTML-based imitation of a desktop. Imitations desktops to-date have simply been clunky and slow imitations of the real-thing at best. Others have been overly slick. But one thing they all have in common: None of them have nailed it. The desktop of the future – what some have called “the Webtop” – still has yet to be invented.

It’s going to be a hosted web service

Is the desktop even going to exist anymore as the Web becomes increasingly important? Yes, there will have to be some kind of interface that we consider to be our personal “home” and “workspace” — but ultimately it will have to be a unified space that all our devices connect to and share. This requires that it be a hosted online service.

Currently we have different information spaces on different devices (laptop, mobile device, PC). These will merge. Native local clients could be created for various devices, but ultimately the simplest and therefore most likely choice is to just use the browser as the client. This coming “Webtop” will provide an interface to your local devices, applications and information, as well as to your online life and information.

Today we think of our Web browser running inside our desktop as an applicaiton. But actually it will be the other way around in the future: Our desktop will run inside our browser as an application.

Instead of the browser running inside, or being launched from, some kind of next-generation desktop web interface technology, it’s will be the other way around: The browser will be the shell and the desktop application will run within it either as a browser add-in, or as a web-based application.

The Web 3.0 desktop is going to be completely merged with the Web — it is going to be part of the Web. In fact there may eventually be no distinction between the desktop and the Web anymore.

The focus shifts from information to attention

As our digital lives shift from being focused on the old fashioned desktop to the Web environment we will see a shift from organizing information spatially (directories, folders, desktops, etc.) to organizing information temporally (feeds, lifestreams, microblogs, timelines, etc.).

Instead of being just a directory, the desktop of the future is going to be more like a feed reader or social news site. The focus will be on keeping up with all the stuff flowing in and out of the user’s environment. The interface will be tuned to help the user understand what the trends are, rather than just on how things are organized.

The focus will be on helping the user to manage their attention rather than just their information. This is a leap to the meta-level: A second-order desktop. Instead of just being about the information (the first-order), it is going to be about what is happening with the information (the second-order).

Users are going to shift from acting as librarians to acting as daytraders.

Our digital roles are already shifting from acting as librarians to becoming more like daytraders. In the PC era we were all focused on trying to manage the stuff on our computers — in other words, we were acting as librarians. But this is going to shift. Librarians organize stuff, but daytraders are focused on discovering and keeping track of trends. It’s a very different focus and activity, and it’s what we are all moving towards.

We are already spending more of our time keeping up with change and detecting trends, than on organizing information. In the coming decade the shelf-life of information is going to become vanishingly short and the focus will shift from storage and recall to real-time filtering, trend detection and prediction.

The Webtop will be more social and will leverage and integrate collective intelligence

The Webtop is going to be more socially oriented than desktops of today — it will have built-in messaging and social networking, as well as social-media sharing, collaborative filtering, discussions, and other community features.

The social dimension of our lives is becoming perhaps our most important source of information. We get information via email from friends, family and colleagues. We get information via social networks and social media sharing services. We co-create information with others in communities.

The social dimension is also starting to play a more important role in our information management and discovery activities. Instead of those activities remaining as solitary, they are becoming more communal. For example many social bookmarking and social news sites use community sentiment and collaborative filtering to help to highlight what is most interesting, useful or important.

It’s going to have powerful semantic search and social search capabilities built-in

The Webtop is going to have more powerful search built-in. This search will combine both social and semantic search features. Users will be able to search their information and rank it by social sentiment (for example, “find documents about x and rank them by how many of my friends liked them.”)

Semantic search will enable highly granular search and navigation of information along a potentially open-ended range of properties and relationships.

For example you will be able to search in a highly structured way — for example, search for products you once bookmarked that have a price of $10.95 and are on-sale this week. Or search for documents you read which were authored by Sue and related to project X, in the last month.

The semantics of the future desktop will be open-ended. That is to say that users as well as other application and information providers will be able to extend it with custom schemas, new data types, and custom fields to any piece of information.

Interactive shared spaces instead of folders

Forget about shared folders — that is an outmoded paradigm. Instead, the  new metaphor will be interactive shared spaces.

The need for shared community space is currently being provided for online by forums, blogs, social network profile pages, wikis, and new community sites. But as we move into Web 3.0 these will be replaced by something that combines their best features into one. These next-generation shared spaces will be like blogs, wikis, communities, social networks, databases, workspaces and search engines in one.

Any group of two or more individuals will be able to participate in a shared space that connects their desktops for a particular purpose. These new shared spaces will not only provide richer semantics in the underlying data, social network, and search, but they will also enable groups to seamlessly and collectively add, organize, track, manage, discuss, distribute, and search for information of mutual interest.

The personal cloud

The future desktop will function like a “personal cloud” for users. It will connect all their identities, data, relationships, services and activities in one virtual integrated space. All incoming and outgoing activity will flow through this space. All applications and services that a user makes use of will connect to it.

The personal cloud may not have a center, but rather may be comprised of many separate sub-spaces, federated around the Web and hosted by different service-providers. Yet from an end-user perspective it will function as a seamlessly integrated service. Users will be able to see and navigate all their information and applications, as if they were in one connected space, regardless of where they are actually hosted. Users will be able to search their personal cloud from any point within it.

Open data, linked data and open-standards based semantics

The underlying data in the future desktop, and in all associated services it connects, will be represented using open-standard data formats. Not only will the data be open, but the semantics of the data – the schema – will also be defined in an open way. The emerigng Semantic Web provides a good infrastructure for enabling this to happen.

The value of open linked-data and open semantics is that data will not be held prisoner anywhere and can easily be integrated with other data.

Users will be able to seamlessly move and integrate their data, or parts of their data, in different services. This means that your Webtop might even be portable to a different competing Webtop provider someday. If and when that becomes possible, how will Webtop providers compete to add value?

It’s going to be smart

One of the most important aspects of the coming desktop is that it’s going to be smart. It’s going to learn and help users to be more productive. Artificial intelligence is one of the key ways that competing Webtop providers will differentiate their offerings.

As you use it, it’s going to learn about your interests, relationships, current activities, information and preferences. It will adaptively self-organize to help you focus your attention on what is most important to whatever context you are in.

When reading something while you are taking a trip to Milan it may organize itself to be more contextually relevant to that time, place and context. When you later return home to San Francisco it will automatically adapt and shift to your home context. When you do a lot of searches about a certain product it will realize your context and intent has to do with that product and will adapt to help you with that activity for a while, until your behavior changes.

Your desktop will actually be a semantic knowledge base on the back-end. It will encode a rich semantic graph of your information, relationships, interests, behavior and preferences. You will be able to permit other applications to access part or all of your graph to datamine it and provide you with value-added views and even automated intelligent assistance.

For example, you might allow an agent that cross-links things to see all your data: it would go and add cross links to relevant things onto all the things you have created or collected. Another agent that makes personalized buying recommendations might only get to see your shopping history across all shopping sites you use.

Your desktop may also function as a simple personal assistant at times. You will be able to converse with your desktop eventually — through a conversational agent interface. While on the road you will be able to email or SMS in questions to it and get back immediate intelligent answers. You will even be able to do this via a voice interface.

For example, you might ask, “where is my next meeting?” or “what Japanese restaurants do I like in LA?” or “What is Sue’s Smith’s phone number?” and you would get back answers. You could also command it to do things for you — like reminding you to do something, or helping you keep track of an interest, or monitoring for something and alerting you when it happens.

Because your future desktop will connect all the relationships in your digital life — relationships connecting people, information, behavior, prefences and applications — it will be the ultimate place to learn about your interests and preferences.

Federated, open policies and permissions

This rich graph of meta-data that comprises your future desktop will enable the next-generation of smart services to learn about you and help you in an incredibly personalized manner. It will also of course be rife with potential for abuse and privacy will be a major function and concern.

One of the biggest enabling technologies that will be necessary is a federated model for sharing meta-data about policies and permissions on data. Information that is considered to be personal and private in Web site X should be recognized and treated as such by other applications and websites you choose to share that information with. This will require a way for sharing meta-data about your policies and permissions between different accounts and applicaitons you use.

The semantic web provides a good infrastructure for building and deploying a decentralized framework for policy and privacy integration, but it has yet to be developed, let alone adopted. For the full vision of the future desktop to emerge a universally accepted standard for exchanging policy and permission data will be a necessary enabling technology.

Who is most likely to own the future desktop?

When I think about what the future desktop is going to look like it seems to be a convergence of several different kinds of services that we currently view as separate.

It will be hosted on the cloud and accessible across all devices. It will place more emphasis on social interaction, social filtering, and collective intelligence. It will provide a very powerful and extensible data model with support for both unstructured and arbitrarily structured information. It will enable almost peer-to-peer like search federation, yet still have a unified home page and user-experience. It will be smart and personalized. It will be highly decentralized yet will manage identity, policies and permissions in an integrated cohesive and transparent manner across services.

By cobbling together a number of different services that exist today you could build something like this in a decentralized fashion. Is that how the desktop of the future will come about? Or will it be a new application provided by one player with a lot of centralized market power? Or could an upstart suddently emerge with the key enabling technologies to make this possible? It’s hard to predict, but one thing is certain: It will be an interesting process to watch.

Here is the full video of my talk on the Semantic Web at The Next Web 2008 Conference. Thanks to Boris and the NextWeb gang!

I have been thinking a lot about social networks lately, and why there are so many of them, and what will happen in that space.

Today I had what I think is a "big realization" about this.

Everyone, including myself, seems to think that there is only room for one big social network, and it looks like Facebook is winning that race. But what if that assumption is simply wrong from the start?

What if social networks are more like automobile brands? In other words, there can, will and should be many competing brands in the space?

Social networks no longer compete on terms of who has what members. All my friends are in pretty much every major social network.

I also don’t need more than one social network, for the same reason — my friends are all in all of them. How many different ways do I need to reach the same set of people? I only need one.

But the Big Realization is that no social network satisfies all types of users. Some people are more at home in a place like LinkedIn than they are in Facebook, for example. Others prefer MySpace.  There are always going to be different social networks catering to the common types of people (different age groups, different personalities, different industries, different lifestyles, etc.).

The Big Realization implies that all the social networks are going to be able to interoperate eventually, just like almost all email clients and servers do today. Email didn’t begin this way. There were different networks, different servers and different clients, and they didn’t all speak to each other. To communicate with certain people you had to use a certain email network, and/or a certain email program. Today almost all email systems interoperate directly or at least indirectly. The same thing is going to happen in the social networking space.

Today we see the first signs of this interoperability emerging as social networks open their APIs and enable increasing integration. Currently there is a competition going on to see which "open" social network can get the most people and sites to use it. But this is an illusion. It doesn’t matter who is dominant, there are always going to be alternative social networks, and the pressure to interoperate will grow until it happens. It is only a matter of time before they connect together.

I think this should be the greatest fear at companies like Facebook. For when it inevitably happens they will be on a level playing field competing for members with a lot of other companies large and small. Today Facebook and Google’s scale are advantages, but in a world of interoperability they may actually be disadvantages — they cannot adapt, change or innovate as fast as smaller, nimbler startups.

Thinking of social networks as if they were automotive brands also reveals interesting business opportunities. There are still several unowned opportunities in the space.

Myspace is like the car you have in high school. Probably not very expensive, probably used, probably a bit clunky. It’s fine if you are a kid driving around your hometown.

Facebook is more like the car you have in college. It has a lot of your junk in it, it is probably still not cutting edge, but its cooler and more powerful.

LinkedIn kind of feels like a commuter car to me. It’s just for business, not for pleasure or entertainment.

So who owns the "adult luxury sedan" category? Which one is the BMW of social networks?

Who owns the sportscar category? Which one is the Ferrari of social networks?

Who owns the entry-level commuter car category?

Who owns equivalent of the "family stationwagon or minivan" category?

Who owns the SUV and offroad category?

You see my point. There are a number of big segments that are not owned yet, and it is really unlikely that any one company can win them all.

If all social networks are converging on the same set of features, then eventually they will be close to equal in function. The only way to differentiate them will be in terms of the brands they build and the audience segments they focus on. These in turn will cause them to emphasize certain features more than others.

In the future the question for consumers will be "Which social network is most like me? Which social network is the place for me to base my online presence?"

Sue may connect to Bob who is in a different social network — his account is hosted in a different social network. Sue will not be a member of Bob’s service, and Bob will not be a member of Sue’s, yet they will be able to form a social relationship and communication channel. This is like email. I may use Outlook and you may use Gmail, but we can still send messages to each other.

Although all social networks will interoperate eventually, depending on each person’s unique identity they may choose to be based in — to live and surf in — a particular social network that expresses their identity, and caters to it. For example, I would probably want to be surfing in the luxury SUV of social networks at this point in my life, not in the luxury sedan, not the racecar, not in the family car, not the dune-buggy. Someone else might much prefer an open source, home-built social network account running on a server they host. It shouldn’t matter — we should still be able to connect, share stuff, get notified of each other’s posts, etc. It should feel like we are in a unified social networking fabric, even though our accounts live in different services with different brands, different interfaces, and different features.

I think this is where social networks are heading. If it’s true then there are still many big business opportunities in this space.

Our present day search engines are a poor match for the way that our brains actually think and search for answers. Our brains search associatively along networks of relationships. We search for things that are related to things we know, and things that are related to those things. Our brains not only search along these networks, they sense when networks intersect, and that is how we find things. I call this associative search, because we search along networks of associations between things.

Human memory — in other words, human search — is associative. It works by “homing in” on what we are looking for, rather than finding exact matches. Compare this to the the keyword search that is so popular on the Web today and there are obvious differences. Keyword searching provides a very weak form of “homing in” — by choosing our keywords carefully we can limit the set of things which match. But the problem is we can only find things which contain those literal keywords.

There is no actual use of associations in keyword search, it is just literal matching to keywords. Our brains on the other hand use a much more sophisticated form of “homing in” on answers. Instead of literal matches, our brains look for things things which are associatively connected to things we remember, in order to find what we are ultimately looking for.

For example, consider the case where you cannot remember someone’s name. How do you remember it? Usually we start by trying to remember various facts about that person. By doing this our brains then start networking from those facts to other facts and finally to other memories that they intersect.  Ultimately through this process of “free association” or “associative memory” we home in on things which eventually trigger a memory of the person’s name.

Both forms of search make use of the intersections of sets, but the associative search model is exponentially more powerful because for every additional search term in your query, an entire network of concepts, and relationships between them, is implied. One additional term can result in an entire network of related queries, and when you begin to intersect the different networks that result from multiple
terms in the query, you quickly home in on only those results that make sense. In keyword search on the other hand, each additional search term only provides a linear benefit — there is no exponential amplification using networks.

Keyword search is a very weak approximation of associative search because there really is no concept of a relationship at all. By entering keywords into a search engine like Google we are simulating an associative search, but without the real power of actual relationships between things to help us. Google does not know how various concepts are related and it doesn’t take that into account when helping us find things. Instead, Google just looks for documents that contain exact matches to the terms we are looking for and weights them statistically. It makes some use of relationships between Web pages to rank the results, but it does not actually search along relationships to find new results.

Basically the problem today is that Google does not work the way our brains think. This difference creates an inefficiency for searchers: We have to do the work of translating our associative way of thinking into “keywordese” that is likely to return results we want. Often this requires a bit of trial and error and reiteration of our searches before we get result sets that match our needs.

A recently proposed solution to the problem of “keywordese” is natural language search (or NLP search), such as what is being proposed by companies like Powerset and Hakia. Natural language search engines are slightly closer to the way we actually think because they at least attempt to understand ordinary language instead of requiring keywords. You can ask a question and get answers to that question that make sense.

Natural language search engines are able to understand the language of a query and the language in the result documents in order to make a better match between the question and potential answers. But this is still not true associative search. Although these systems bear a closer resemblance to the way we think, they still do not actually leverage the power of networks — they are still not as powerful as associative search.

A natural language search can understand the meaning of a query like “books about Harry Potter” and it knows this is not the same as “Books by Harry Potter.” But ultimately what is happening is that a linguistic expression is being converted into a more sophisticated keyword search. The language in the query is being mapped to documents that contain text that answers a question, or to data objects that match the thing being asked for. This is certainly better than keyword search but it is still ultimately just a smarter form of literal matching. It is not really making use of associative search along networks of semantic relationships in the data (other than linguistic relationships between words in the query) or any sort of sophisticated reasoning.

By comparison, associative search doesn’t merely understand the meaning of the query, it understands and can reason about relationships in the data. This is an important distinction.

An associative search returns documents that represent things that are related, via various forms of associations (semantic links), to the things in the query. An associative search looks through a network of associations for the things that are most connected to the items in the query. By specifying more specific starting points, the set of things which are connected to all those starting points is narrowed. Thus an associative search is an intersection of multiple networks. The items that are most strongly intersected are the results that are most likely
to matter.

Associative search is a very different approach to search from keyword search (which merely looks for things with the keywords in them) and natural language search (which merely looks for things that contain content that matches the meaning of the question). It also happens to be more similar to how our brains actually think.

On its own, associative search represents an important advance in the way we search. But by adding some simple reasoning to an associative search it becomes even more powerful. Reasoning adds the ability to generalize or get more specific, and to weight various paths through the network of relationships in more sophisticated ways, such as based on logical relationships or inferences through the network.

A simple example of reasoning is transitivity — for example, if A is a part of B and B is a part of C, then A is a part of C. If we know that the “part of” relationship is transitive, then whenever we see chains of “part of” links between things we can make transitive inferences. In an associative search these inferences are quite useful. For example, we can search for all the parts of a 747 jet. Using transitive reasoning along networks of relationships we can find all the parts, even those things that are “parts of parts.” Similarly we could find “all products of Sony” including products of subsidiaries and business units of Sony. Transitive inferences across transitive links is just one type of reasoning; there are many other variations
that are possible, which when combined together become even more useful.

Our current search tools — whether they are keyword based or natural language based do not support true associative search, let alone reasoning. But we do see associative search starting to appear in a very different breed of application: social networks. A search in LinkedIn for example, is an associative search. Will social networks do an end-run around traditional search engines to provide the next-generation of search? It’s quite possible. Facebook and LinkedIn are far better positioned than Google today for associative search. In fact, I would venture that this is how Facebook could give Google some serious competition. But they have to hurry if they are going to do this — Google has clearly realized the power of “social search” and is rapidly moving to leverage it in their own search results.

Ultimately associative search is more than just social search however. To be really effective, associative search engines need to understand and leverage the full spectrum of relationships between things, not just social relationships. They need to see and understand more types of relationships between more types of things. In order to accomplish this, associative search engines need the Semantic Web.

The Semantic Web provides exactly what is needed to enableassociative search, with reasoning, on the Web-at-large. Using RDF and OWL, content can be marked up with metadata that specifies not only its intended meaning and structure, but also the various kinds of semantic relationships it has to other content and to other concepts. In other words, these standards provide a way to add a new network of
semantically defined associations to the data on the Web. For example a document about Microsoft can be linked to the concepts “Software Company,” “Software Manufacturer,” and “Redmond.” It can also be linked to a data record that represents “Microsoft” and the properties that define it as a company. The “Microsoft” object can then link to companies that are “suppliers” and “customers” and “competitors” as well as to things which are connected as “products” or “services.”

This rich network of relationships between things goes far beyond documents. It contains relationships to people, places, other organizations, products, events, services, etc. It’s similar to a social network, but instead of just containing people and social relationships, it contains more types of things and relationships between them. This is really what the Semantic Web enables. One can imagine that as this new semantic data becomes visible on the Web (which is rapidly happening in fact), the power of search will be dramatically improved. Associative search is coming soon to a Web near you!

With that in mind, here is an example of how Semantic Web enabled associative search will work in the future.

PROBLEM: I am trying to remember name of the organizer of a conference I once attended.

WHAT I ALREADY  KNOW:

• I know this person and have corresponded with them in the past.
• The conference was related to government and the Internet.
• It took place in a town near Big Sur, but I can’t remember the name of the town.
• The organizer of the conference once introduced me to a male celebrity, but I can’t remember the celebrity’s name.
• I gave a talk at the Conference about Web 3.0.
• My friend, Sue Smith, also spoke at the conference.
• The conference I attended took place in the Spring, but I am not sure if it was last year or two years ago.

In the above example, I cannot remember the specific keywords that will help me generate a query to find the answer. Instead, I remember a number of relationships and generalizations about the answer. Present day search engines cannot see these relationships, and they have no ability to understand a generalization and look at things it contains.

The ability to intersect the sets formed by relationships and generalizations is a fundamental feature of human memory and search. But our present day tools don’t have these capabilities. Thus we have to spend time translating our questions into keywordese, rather than just asking our questions in the actual language of human thought.

There are two ways to approach solving this.

1. The first way is to create artificial intelligence which, given a question in natural language English, can understand it and reason about the question as well as understand and reason about the information in the set of documents being searched, in order to intelligently arrive at candidate answers. This is computationally intensive, and very hard to program. This is why AI hasn’t quite happened yet on this scale.
2. A perhaps easier approach is to use the Semantic Web. In the Semantic Web approach, metadata is embedded into content that describes the meaning of the content, it’s various important properties, and its relationships to other concepts. On the basis of this metadata, the problem becomes much simpler to solve. Instead of doing high-level AI it becomes essentially a statistical search.

Now let’s look at how using the Semantic Web could help us solve the above problem via an associative search:

Items are connected to more general or specific concepts by virtue of semantic linkages between concepts. For example, the conference I am looking for is related to the concepts “Government” and “Technology.” If I can at least remember that then I can find conferences related to government and technology. Furthermore, since the concept “Policy” is a subset of government it may be related to that topic
as well.

Likewise, things are connected to things that are “near” them via geographic links. Because the conference was near Big Sur it is in Northern California, along the coast. It is probably in a town that is geographically close, ror example Carmel-by-the-Sea is a town that is near the Big Sur area.

The organizer of the conference introduced me to a male celebrity. There are several celebrities in my social network. If the fact that I met certain people via introductions from other people was stored using semantic links, then this too would be searchable. For example, “find all celebrities I was introduced to by my connections” would be a solvable query. Similarly, “find people who introduced me to celebrities” would also be solvable.

The fact that I gave a talk at the conference could also be semantically represented on a data record describing the conference, as well as on my own profile. Thus there could exist a link such as “speaker at” which links me to various conferences I have spoken at. I could then get a list of all the conference I have spoken at. I could also look for all the conferences where both myself and Sue Smith were speakers.

Or, better yet, there could be a link called “Gave talk about” which links me to an instance describing each talk I have given. From such an instance there could then be “Gave talk at” links to all the events where I have given that talk. So I could look up my “Web 3.0″ talk and then see all the conferences where I gave that talk.

Temporal relations can also be generalized and semantically represented. For example, the conference I am looking for took place in the spring. Therefore only look for conferences that took place in or near months that are considered to be in the spring season.

By intersecting the results of the above searches we narrow down very precisely to a set of people I might be looking for, or just to a single qualifying person.

For example the answer I was seeking for was that the organizer was named Robert Jones, and the conference was about Government and Technology Policy in Carmel-by-the-Sea last spring. This result should be easily findable via associative search starting from the above set of things I remember.

But if for some reason the answer is still not there, there is another capability which the brain uses that we need to add to our search engines: Perturbation, or what could be called “prospecting.”

The query I entered is comprised of a question and a set of facts related to the answer I am seeking. But there is a possibility that I asked the question incorrectly, or some of the facts I added were incorrect, or insufficient. Perturbation can correct for this by introducing variations into the question and the facts in order to explore the space of answers that are “near” them as well.

There are many ways to go about adding perturbation to the system — for example, we can search more than one hop out from every link, or we can search for other types of relationships that are highly correlated with relationships we are asking for
explicitly, or we can include results for things which are strongly connected to things that are found.

From a user-interface standpoint perturbation can be controlled with a simple “sliding lever” in the user interface for “Precision.” If the user sets very high Precision as a requirement then there is no perturbation — the results are exact matches to the query and facts. If there is low Precision as a requirement then there can be more perturbation, thus the results are fuzzy and may include things that are near what I asked for but not exactly what I specified, enabling me to discover
things via relevant relationships that I could not even remember to mention as facts.

Finally, using a reasoner, the results found by the above search can be analyzed such that those results which are most likely to be what I am looking for, given the facts I have included as constraints, are presented first. Reasoning becomes the ranking algorithm in the system, rather than something like Pagerank. The answers that actually make the most sense in the context of my question are delivered first.

The above illustration describes how searches that are powered by the Semantic Web will work, once this technology is widely adopted. This is how the brain works, and how our search engines should work as well.

This is not a pipedream — in fact it is already happening in research settings and in the government. Within 15 years, if not a lot sooner, we will see these capabilities emerge in consumer-grade search interfaces.

This is a video of my talk at the Digital Now conference in Orlando yesterday. There’s a long intro by Don Dea, and then I speak (starting at index 05:14) about the Semantic Web and Twine.

I highly recommend this new book on Collective Intelligence. It features chapters by a Who’s Who of thinkers on Collective Intelligence, including a chapter by me about “Harnessing the Collective Intelligence of the World Wide Web.”

Here is the full-text of my chapter, minus illustrations (the rest of the book is great and I suggest you buy it to have on your shelf. It’s a big volume and worth the read):

Harnessing the
collective intelligence

of the
World-Wide Web

Nova Spivack[1]

Introduction

We are about to enter the third decade of the Web, sometimes referred to as “Web 3.0.” During this decade, the Web will evolve from a globally distributed fileserver into a globally distributed database. This shift will be enabled by a set of emerging technologies called The Semantic Web, which add a new layer of machine-understandable metadata about the meaning of information to the content of the Web.

The Semantic Web will catalyze a new era in collective intelligence. Individuals, groups, organizations and communities will be able to create, connect, find and share knowledge more intelligently and productively than ever before. Ultimately it will enable the Web itself, and all the people and applications that participate in it, to become more collectively intelligent.

Web 3.0—The Third Decade of the Web

The third-decade of the Web, “Web 3.0,” begins officially in 2010, but we are already entering the early stages of this transition today. To understand where the Web is headed it helps to zoom out to a larger historical context.

The final decade of the PC-era (1980—1990) was largely concerned with innovation on the front-end of the personal computer: the desktop and user interface layer of the PC. The focus of this period was in making PC’s easier to use with innovations such as Microsoft Windows, the Macintosh user-interface, and more consistent
user-interfaces and integration across applications.

The first decade of the Web-era (“Web 1.0” from 1990 – 2000), was focused on the back-end of the Web: the core technologies and platforms of the Web such as HTML, HTTP, Web servers, search engines, commerce technologies, advertising technologies, and the basic architectures and business model of Web applications. This decade was mainly focused on the technology and infrastructure of the Web and most of the actual innovation dollars were spent on making things that only software developers could see.

In contrast, the second decade of the Web (“Web 2.0” from 2000—2010) has been largely focused on the front-end of the Web. Much of the innovation has not been on actual technology but rather on design patterns and user-interfaces for improving the end-user experience of the Web. During this decade we have focused on paradigms such as AJAX, which is a set of technologies and design methodologies for making Web sites more visually appealing and interactive.

Another big focus of Web 2.0 has been user-generated content, and in particular the practice of “tagging” content with subject tags. Tagging has in turn led to the concept of “folksonomies” in which taxonomies that organize data are evolved in a
bottom-up fashion by a decentralized community of users.

The coming third-decade of the Web (“Web 3.0” from 2010—2020) will shift the emphasis back to the back-end of the Web. This decade will be largely focused on upgrading the technical infrastructure and content of the Web, based on emerging
technologies such as the Semantic Web. During this decade the primary push will
be enriching the Web so that it can function more like a database.

Today the Web is composed mainly of unstructured and semistructured data such as text files and Web pages. Keyword search engines are able to provide rudimentary search capabilities over this information, but only for the most simplistic queries. Compare current Web search to the more precise capabilities of queries against a database and the difference is immediately clear. The Web does not provide anything close to the search capabilities or precision of a database today. But that is about to change.

The Semantic Web provides a way to enrich both unstructured and structured data so that it can be queried with the precision of a database. Essentially, it provides a way to tag any information with metadata that explains what it means—and this metadata can be understood by software applications, such as search engines or knowledge management applications. It’s important to note that The Semantic Web is not a new Web, it’s just a new layer of the Web we already have. The semantic metadata that comprises the knowledge of the Semantic Web won’t live in some new place—it lives right in the existing documents and data on the Web. The
knowledge of the Semantic Web is encoded using special new markup languages
such as RDF and OWL.

This metadata is invisible to users (it doesn’t appear in Web browsers) but behind the scenes it can be read by any application that is compatible with these markup languages. So when any application, such as a next-generation search engine, sees a Web page or data record that contains RDF or OWL metadata, it can then use that
metadata to understand what that page or data record means, is about, what it is
related to, and how to interpret it. With Semantic Web metadata in place, searches on the Web will be as, or even more, precise as those in any database. But that is just the beginning of what the Semantic Web enables. Beyond merely improving search, the Semantic Web actually transforms the Web into a database—a worldwide database in which data records can be moved around, shared, and linked together in new ways.

On the basis of the technologies of The Semantic Web and the Web 3.0 era, we will then be able to enter the fourth decade of the Web (“Web 4.0”—2020—2030) in which the shift will turn back to the front-end of the Web. The Semantic Web doesn’t just add metadata about the meaning of information to the Web, it also enables metadata to be added about relationships, conceptual linkages, logical connections, and even logical rules. On the basis of this additional metadata, Web users and other applications will be able to harness the power of intelligent agents that will search the Web for things that interest them, make suggestions and recommendations, and even potentially transact on their behalf. This will open the door to a new kind of user-interface to the Web that is smarter and more conversational in nature, in which users will enter into dialogues with agents and interact with them search the Web and make decisions. A conversational interface to the Web will be more appropriate in the increasingly mobile world, when users will mostly interact with the Web from small portable mobile or embedded devices.

Users on mobile devices that have little to no screen real-estate will need a more productive way to interact with the Web than through a miniature browser; nobody like sorting through pages of Google results on a cell phone. Instead, they will want to simply ask a question (perhaps through a voice interface, rather than typing with their thumbs) and have a virtual intelligent assistant dispatch agents to find the best answers and then report back to them with results or to ask further questions or for a decision.

Smart, interactive conversational interfaces and intelligent agent-based virtual assistants are possible today, but only in narrow domains. In the Web 4.0 era they may in fact be our primary way of interacting with the whole Web and may be built into the user interface of most search engines, personal email providers, and leading Websites.

The Virtualization of Knowledge and Intelligence

In the long-term, the Semantic Web provides a way to move much of the “intelligence” that currently resides in the minds of individuals, groups and organizations, and/or that is hard-coded into various software and Web applications, out onto the Web itself. It provides a way to virtualize knowledge and intelligence in an explicitly machine-readable, universally accessible form. In other words, it provides a way to start making the Web “smarter.”

Knowledge and expertise that previously only existed in people’s heads, or had to be painstakingly coded into each particular vertical software application, will be
represented in a form of universally readable metadata on the Web—just like HTML documents today. In other words, using the Semantic Web you can publish
knowledge and even the underlying conceptual frameworks, rules and heuristics
that embody domain expertise, on the Web in an abstract, machine-readable form.

There are many benefits that stem from this. For one thing, it will make it much easier to write smart software applications because much of the necessary “smarts” will not reside in the applications at all, but will rather live out there on the Web.

For example, to write an application that can intelligently assist with travel logistics, a developer will simply be able to point it at existing sets of knowledge and rules that exist for the travel domain on the Web already. The application will
be able to draw on those pools of existing domain-knowledge without having to be specifically programmed to do so, because it understands the underlying standards of the Semantic Web. Similarly, the same application could just as easily help someone trade on the stock market, by simply pointing to domain knowledge on Semantic Web about finance and investment.

As more pools of domain knowledge are added to the Web around various verticals, all applications will potentially benefit. This sets up a kind of network effect in which a global knowledge commons begins to form and self-amplify over time. For
example, first the travel domain is added to the Semantic Web. Then someone else adds domain knowledge about geography and links them together. Another group then adds domain knowledge about hotels, and another one adds domain knowledge about weather—and these all connect to each other in various ways.

With all of this interconnected knowledge on the Web in machine-readable form, application developers can then more easily and quickly write applications that understand concepts and rules related to booking travel reservations, and that can
cross-reference reservation information with knowledge about geographic places,
relevant weather, and hotels in those locations. And in the other direction, someone booking a hotel can then find information about relevant weather and
book travel to get to that hotel. This is just one example. There are an infinite range of other possibilities for these technologies across all domains.

The key point of all this is that The Semantic Web enables applications to become thinner, yet at the same time smarter, by drawing on the collective intelligence embodied by the Web itself. It will become possible to write applications that understand one or more specialized vertical domains faster, and ultimately applications will become more general—they will be able to dynamically load in specialized domain knowledge for whatever domain is needed, without having to be
specifically programmed or limited to just those domains.

Application developers will be able to draw on the knowledge added to the Web by others, instead of having to reinvent the wheel by programming all that knowledge
directly into their applications every time. And in turn, the knowledge that their applications create can, if they want to allow it, be published back onto the Web for other applications to draw on as well.

Semantic Web as The Next Leap in Human Collective
Intelligence

Looking at the evolution of the Semantic Web in historical context, we can view it as the next big step in a longer process of the evolution of human collective intelligence.

Before the invention of written language, knowledge could only be communicated verbally and was handed down through oral traditions. During this period, one had to be in immediate physical proximity of someone who had certain knowledge in order to receive it from them. This meant that the maximum effective range of human collective intelligence was quite short in space and time.

With the invention of writing, and eventually printing, humanity was able to process knowledge over longer distances in space and time, and with less reliance on particular individuals. People could now engage in dialogues and dialectics with larger groups of people in more places, across larger distances in space, and with
more precision over larger ranges of time.

The printing press took this to a new level by starting the process of mass-distribution of knowledge, but it still relied on an expensive physical manufacturing process and a paper medium that was perishable and costly to store and move around.

With advent of electronic communications of various forms, humanity achieved many milestones—the transmission of knowledge could take place at the speed of
light, and using digital storage media we were freed from the limitations of
the paper medium.

The Internet and the Web transformed the process of distributing knowledge even further—enabling a global knowledge commons to emerge. The Internet and Web enable anyone and everyone to become providers of knowledge, not just consumers—a fundamental shift in the way that knowledge transmission and media function. They are not just about the mass-distribution and mass-consumption of knowledge; they enable the mass-creation of knowledge. In some respects these technologies are analogues of the printing press in that they have democratized the process of creating, sharing and accessing knowledge by fundamentally changing the economics of the entire process—making it affordable and accessible to all.

But even on the Web, for all its many benefits, knowledge is still not free from the
limitations of the human brain. Only humans can really understand the knowledge
that is represented in Web sites and databases, for example. While all other processes related to the distribution, storage and access to knowledge can now
be done digitally, using software and the Web, the processes of creating, consuming and actually understanding knowledge are still limited only to living humans. That’s where the Semantic Web comes in.

Liberating Knowledge and Intelligence from Human
Brains

The Semantic Web virtualizes human knowledge and expertise outside of human brains, and even outside of any particular software application—knowledge becomes essentially just more data on the Web. When we speak of knowledge here we don’t just mean information—the first-order raw data that is currently on the Web—we mean the actual meaning and interpretation of the information that is not on the Web but rather exists only in human brains.

The Semantic Web provides a way to make the meaning and interpretation of information explicit in a form that is unambiguous and publishable, and shareable, on the Web. This will make all this knowledge understandable by software. It’s almost like the invention of a new language—a sort of meta-language for formally expressing what exactly you mean when you say something. The impact of this could be enormous.

For the first time in human history, we won’t have to rely only on humans to create, understand and consume knowledge—our machines will be able to help us do this. They will help us work, collaborate, create, explore, monitor, discover, search, innovate, connect, and synthesize. This will open the door to an almost unimaginable amplification of the human mind, and human collective intelligence
on this planet. At first the impact of this will largely be focused around assisting humans with simple clerical and research tasks, but the process will inevitably continue to evolve to a point where software will begin to originate new knowledge for us, advise us, and eventually to even start making certain types of decisions on our behalf.

Although the Semantic Web has barely moved from the lab to the mainstream Internet, it is in fact much farther along than most people realize. Today there are already semantic applications under development that can organize all your information automatically, make recommendations based on your dynamically changing interests, identify new connections between ideas or documents in different places, make logical inferences or discover contradictions, and even make
discoveries by doing proofs and explorations based on available data.

Within a few years these capabilities will begin to filter out to the mainstream users of the Internet, and with a decade or two at most, they will become commonplace. There are only a few billion humans today, and each of us can only cope with a small amount of information and relationships before we become overloaded. But in an era of machine understanding of human knowledge we may potentially be able to leverage thousands to millions of software agents to help us. This will vastly
increase our ability to cope with masses of information and relationships productively. In an increasingly complex, distributed, and rapidly changing world, we simply will not be able to cope in the future without help. The Semantic Web provides one path to solving these problems, enabling us to remain productive in the future.

Amplifying Human Collective Intelligence

The Semantic Web does not replace humans or take them out of the equation. It simply reduces the load on humans, freeing them from some of the pain of information overload, and providing a new path for software to begin to augment and even amplify human collective intelligence.

Today there are several barriers to human collective intelligence that arise from basic limitations of the human brain. Human individuals, and groups of humans, simply cannot process or share knowledge effectively beyond a certain level of
information or relationship complexity and change. For this reason, collaboration and collective intelligence are often easier to achieve and yield better results in small groups than large groups.

As group size increases, productive collective intelligence becomes dramatically harder to achieve. Thus, ironically even though larger groups offer the potential for
exponential increases in collective intelligence, in practice the opposite is usually the result: the larger teams get, the dumber they get. An entire industry of management consultants and facilitators exists because of these inefficiencies.

The Semantic Web may be able to help with this age-old problem. By enabling software to understand information and relationships, we may be able to begin to
automatically and intelligently facilitate interpersonal and group collaboration and knowledge management, and this may finally enable larger groups to become exponentially smarter instead of dumber.

Twine.com—A New Service for Collective Intelligence

My own company, Radar Networks, has recently introduced a new service based on the Semantic Web, called Twine (www.twine.com) that focuses on amplifying human collective intelligence. Twine helps individuals and groups manage and share knowledge more productively, using the Semantic Web.

As people use Twine it learns from them and automatically organizes and connects their information with other related information, saving them valuable time and enabling them to discover connected knowledge. Twine provides individuals and groups with a smart virtual environment for their knowledge.

Twine works with all kinds of knowledge—email, RSS, Web pages, documents, photos, videos, audio, contact records, or anything else. Regardless of where information actually resides, Twine enables users to view it as if it were in one place, and to see how it is connected and organized. Twine also automatically helps to make sense of information and to make it more easily searchable.

Twine is a Web-based online service that is completely built using the Semantic Web. Although it is only in early beta-testing at the time of this writing, it is already
demonstrating that intelligent machine-augmentation of individual and group knowledge management is possible and improves productivity and collaboration.

As Twine unfolds and spreads to more individuals, groups and teams, and organizations and communities, it has the potential to become a new backbone for collective intelligence and knowledge sharing worldwide. At least that is the vision of the project. Time will tell whether we succeed it.

From Global Knowledge Commons to Global Brain

If the Semantic Web develops as predicted, it is possible that within 20 years much, if not all, human knowledge will be represented on the Web in machine-understandable form. We have seen the beginnings of this trend with services such as the Wikipedia. More recently, another initiative called the DBpedia is creating a Semantic Web version of the Wikipedia. But this is just the start of this trend.

As more and more applications and services start producing Semantic Web metadata and exposing it back to other applications and services on the Web, we will begin to create a new global knowledge commons. At first these different services will function like islands of knowledge, but then they will begin to interconnect.

A piece of knowledge in one place will link to and from pieces of knowledge in other
places. Eventually this will become a giant associative network, not so unlike the brain, but on a global scale. And as people and applications surf through its connections and consume its knowledge, adding new knowledge and connections
back to it as they do, it will change and self-organize dynamically. Just as the first generations of the Web have enabled a global medium for “hypertext,” the Semantic Web will enable a global medium for “hyperdata.”

As one projects the future evolution of the Web and the emerging Semantic Web, one cannot help but notice certain similarities to the human mind. Some have even ventured to call this the beginning of an emerging “Global Brain.” It is too early to tell how similar it will truly be to the actual human brain. However we can already
predict with confidence that it will a system that collectively will be capable of at least rudimentary learning, memory, perception, planning and reasoning.

The human brain is a massively parallel collective intelligence engine in which billions of neurons interact across trillions of connections to process and generate
knowledge.

Similarly, the collective intelligence of the Web will involve the combined interactions and intelligence of billions of humans and machines across trillions of
relationships. These processes will not be guided centrally, and the system will most likely not be centralized around a single construct of a “self” nor will it have anything like a human body.

While it will be possible to say the system as a whole is intelligent, it will be difficult to locate any particular source of that intelligence; the intelligence will come from everywhere: from the humans, the software and even the data and links that comprise the Web.

Because the Web is quite different from the human brain, it is likely that its intelligence will be different from what we think of as human intelligence today. But it will nonetheless be intelligent—in a massively distributed, emergent, and chaotic way that we humans may not be able to even comprehend. The “thoughts” the Web will think may be just too vast and complex for us to even recognize, let alone imagine or understand. Yet perhaps in decade-long time-scales at least, we will begin to be able to see the outlines of its thinking.

This is a five minute video in which I was asked to make some predictions for the next decade about the Semantic Web, search and artificial intelligence. It was done at the NextWeb conference and was a fun interview.

Learning from the Future with Nova Spivack from Maarten on Vimeo.

Earlier this month I had the opportunity to visit, and speak at, the Digital Enterprise Research Institute (DERI), located in Galway, Ireland. My hosts were Stefan Decker, the director of the lab, and John Breslin who is heading the SIOC project.

DERI has become the world’s premier research institute for the Semantic Web. Everyone working in the field should know about them, and if you can, you should visit the lab to see what’s happening there.

Part of the National University of Ireland, Galway. With over 100 researchers focused solely on the Semantic Web, and very significant financial backing, DERI has, to my knowledge, the highest concentration of Semantic Web expertise on the planet today. Needless to say, I was very impressed with what I saw there. Here is a brief synopsis of some of the projects that I was introduced to:

• Semantic Web Search Engine (SWSE) and YARS, a massively scalable triplestore.  These projects are concerned with crawling and indexing the information on the Semantic Web so that end-users can find it. They have done good work on consolidating data and also on building a highly scalable triplestore architecture.
• Sindice — An API and search infrastructure for the Semantic Web. This project is focused on providing a rapid indexing API that apps can use to get their semantic content indexed, and that can also be used by apps to do semantic searches and retrieve semantic content from the rest of the Semantic Web. Sindice provides Web-scale semantic search capabilities to any semantic application or service.
• SIOC – Semantically Interlinked Online Communities. This is an ontology for linking and sharing data across online communities in an open manner, that is getting a lot of traction. SIOC is on its way to becoming a standard and may play a big role in enabling portability and interoperability of social Web data.
• JeromeDL is developing technology for semantically enabled digital libraries. I was impressed with the powerful faceted navigation and search capabilities they demonstrated.
  
• notitio.us. is a project for personal knowledge management of bookmarks and unstructured data.
• SCOT, OpenTagging and Int.ere.st.  These projects are focused on making tags more interoperable, and for generating social networks and communities from tags. They provide a richer tag ontology and framework for representing, connecting and sharing tags across applications.
• Semantic Web Services.  One of the big opportunities for the Semantic Web that is often overlooked by the media is Web services. Semantics can be used to describe Web services so they can find one another and connect, and even to compose and orchestrate transactions and other solutions across networks of Web services, using rules and reasoning capabilities. Think of this as dynamic semantic middleware, with reasoning built-in.
• eLite. I was introduced to the eLite project, a large e-learning initiative that is applying the Semantic Web.
• Nepomuk.  Nepomuk is a large effort supported by many big industry players. They are making a social semantic desktop and a set of developer tools and libraries for semantic applications that are being shipped in the Linux KDE distribution. This is a big step for the Semantic Web!
• Semantic Reality. Last but not least, and perhaps one of the most eye-opening demos I saw at DERI, is the Semantic Reality project. They are using semantics to integrate sensors with the real world. They are creating an infrastructure that can scale to handle trillions of sensors eventually. Among other things I saw, you can ask things like "where are my keys?" and the system will search a network of sensors and show you a live image of your keys on the desk where you left them, and even give you a map showing the exact location. The service can also email you or phone you when things happen in the real world that you care about — for example, if someone opens the door to your office, or a file cabinet, or your car, etc. Very groundbreaking research that could seed an entire new industry.

In summary, my visit to DERI was really eye-opening and impressive. I recommend that major organizations that want to really see the potential of the Semantic Web, and get involved on a research and development level, should consider a relationship with DERI — they are clearly the leader in the space.

Special offer to readers of my blog…

There are now well over 30,000 users in the queue to get into the Twine beta. We’re going to start letting people in from the waiting list in waves and it should take about a month or two to let everyone in.

But what good is a waiting list if there’s no way to cut to the front, right? Fortunately, there is a way to skip ahead to the front of the line…

Write a blog post about Twine on your blog and why you want early access, and send me the link to nova (at) radarnetworks (dot) com. along with your first name, last name, and email address. If I like your post, I’ll get you an early access VIP pass to front of the line.

See you in Twine!

Carla Thompson, an analyst for Guidewire Group, has written what I think is a very insightful article about her experience participating in the early-access wave of the Twine beta.

We are now starting to let the press in and next week we will begin to let waves of people in from our over 30,000 user wait list. We will be letting people into the beta in waves every week going forward.

As Carla notes, Twine is a work in progress and we are mainly focused on learning from our users now. We have lots more to do, but we’re very excited about the direction Twine is headed in, and it’s really great to see Twine getting so much active use.

Vewr

I’m here at the BlogTalk conference in Cork, Ireland with a range of bloggers and technologists discussing the emerging social Web. Including myself, Ian Davis and Paul Miller from Talis, there are also a bunch of other Semantic Web folks including Dan Brickley, and a group from DERI Galway.

Over dinner a few of us were discussing the terms “Semantic Web” versus “Web 3.0″ and we all felt a better term was needed. After some thinking, Ian Davis suggested “Web 3G.” I like this term better than Web 3.0 because it loses the “version number” aspect that so many objected to. It has a familiar ring to it as well, reminding me of the 3G wireless phone initiative. It also suggests Tim Berners-Lee’s “Giant Global Graph” or GGG — a synonym for the Semantic Web. Ian stayed up late and put together a nice blog post about the term, echoing many of my own sentiments about how this term should apply to a decade (the third decade of the Web), rather than to a particular technology.

Scoble came over and filmed a full conversation and video demo of Twine. You can watch the long version (1 hour) or the short version (10 mins) on his site. Here’s the link.

Now that I have been asked by several dozen people for the slides from my talk on "Making Sense of the Semantic Web," I guess it’s time to put them online. So here they are, under the Creative Commons Attribution License (you can share it with attribution this site).

You can download the Powerpoint file at the link below:

Download nova_spivack_semantic_web_talk.ppt

Or you can view it right here:

| View | Upload your own

Enjoy! And I look forward to your thoughts and comments.

The New Scientist just posted a quick video preview of Twine to YouTube. It only shows a tiny bit of the functionality, but it’s a sneak peak.

We’ve been letting early beta testers into Twine and we’re learning a lot from all the great feedback, and also starting to see some cool new uses of Twine. There are around 20,000 people on the wait-list already, and more joining every day. We’re letting testers in slowly, focusing mainly on people who can really help us beta test the software at this early stage, as we go through iterations on the app. We’re getting some very helpful user feedback to make Twine better before we open it up the world.

For now, here’s a quick video preview:

Google’s recent announcement of their OpenSocial API’s appears to be a new form of middleware for connecting social networks together. But it’s too early to tell, since the technical details are not available yet. The notion of a middleware service for connecting social networks and sharing data between them makes a lot of sense, and if Google has really made it "open" then it could be very useful. The question remains of course, why would Google do this unless there is some way they have a unique benefit from it? My guess is that they will run advertising through this system, and will have unique advantages in their ability to target ads to people based on the social network profiles they can see via this system. We’ll have to wait and see what happens, but it is interesting.

From the perspective of Radar Networks and Twine.com, this is a trend we are watching closely. It could be something to integrate with, but until we really see the technical details we’ll reserve judgement.

Dan Farber has an interesting piece today about how user-contributed metadata will revolutionize online advertising. He mentions Facebook, Metaweb and Twine as examples. I agree, of course, with Dan’s thoughts on this, since these are some of the underlying motivations of Twine. The rich user-generated metadata in Twine is not just about users however, it’s about everything — products, companies, events, places, web pages, etc. The "semantic graph" we are building is far richer than a graph that is just about people. I’ll be blogging more about this in the future.

Last night I saw that the video of my presentation of Twine at the Web 2.0 Summit is online. My session, "The Semantic Edge," featured Danny Hillis of Metaweb demoing Freebase, Barney Pell demoing Powerset, and myself Demoing Twine, followed by a brief panel discussion with Tim O’Reilly (in that order). It’s a good panel and I recommend the video, however, the folks at Web 2.0 only filmed the presenters; they didn’t capture what we were showing on our screens, so you have to use your imagination as we describe our demos.

An audio cast of one of my presentations about Twine to a reporter was also put online recently, for a more in-depth description.

What a week it has been for Radar Networks. We have worked so hard these last few days to get ready to unveil Twine, and it has been a real thrill to show our work and get such positive feedback and support from the industry, bloggers, the media and potential users.

We really didn’t expect so much excitement and interest. In fact we’ve been totally overwhelmed by the response as thousands upon thousands of people have contacted us in the last 24 hours asking to join our beta, telling us how they would use Twine for their personal information management, their collaboration, their organizations, and their communities. Clearly there is such a strong and growing need out there for the kind of Knowledge Networking capabilities that Twine provides, and it’s been great to hear the stories and make new connections with so many people who want our product. We love hearing about your interest in Twine, what you would use it for, what you want it to do, and why you need it! Keep those stories coming. We read them all and we really listen to them.

Today, in unveiling Twine, over five years of R&D, and contributions from dozens of core contributors, a dedicated group of founders and investors, and hundreds of supporters, advisors, friends and family, all came to fruition. As a company, and a team, we achieved an important milestone and we should all take some time to really appreciate what we have accomplished so far. Twine is a truly ambitious and pardigm-shifting product, that is not only technically profound but visually stunning — There has been so much love and attention to detail in this product.

In the last 6 months, Twine has really matured into a product, a product that solves real and growing needs (for a detailed use-case see this post). And just as our product has matured, so has our organization: As we doubled in size, our corporate culture has become tremendously more interesting, innovative and fun. I could go on and on about the cool things we do as a company and the interesting people who work here. But it’s the passion, dedication and talent of this team that is most inspiring. We are creating a team and a culture that truly has the potential to become a great Silicon Valley company: The kind of company that I’ve always wanted to build.

Although we launched today, this is really just the beginning of the real adventure. There is still much for us to build, learn about, and improve before Twine will really accomplish all the goals we have set out  for it. We have a five-year roadmap. We know this is a marathon, not a sprint and that "slow and steady wins the race." As an organization we also have much learning and growing to do. But this really doesn’t feel like work — it feels like fun — because we all love this product and this company. We all wake up every day totally psyched to work on this.

It’s been an intense, challenging, and rewarding week. Everyone on my team has impressed me and really been at the top of their game. Very few of us got any real sleep, and most of us went far beyond the call of duty. But we did it, and we did it well. As a company we have never cut corners, and we have always preferred to do things the right way, even if the right way is the hard way. But that pays off in the end. That is how great products are built. I really want to thank my co-founders, my team, my investors, advisors, friends, and family, for all their dedication and support.

Today, we showed our smiling new baby to the world, and the world smiled back.

And tonight , we partied!!!

My company, Radar Networks, has just come out of stealth. We’ve announced what we’ve been working on all these years: It’s called Twine.com. We’re going to be showing Twine publicly for the first time at the Web 2.0 Summit tomorrow. There’s lot’s of press coming out where you can read about what we’re doing in more detail. The team is extremely psyched and we’re all working really hard right now so I’ll be brief for now. I’ll write a lot more about this later.

Twine

News Flash!

My company, Radar Networks, is coming out of stealth this Friday, October 19, 2007 at the Web 2.0 Summit, in San Francisco. I’ll be speaking on "The Semantic Edge Panel" at 4:10 PM, and publicly showing our Semantic Web online service for the first time. If you are planning to come to Web 2.0, I hope to see you at my panel.

Here’s the official Media Alert below:

(PRWEB)
October 15, 2007 — At the Web2.0 Summit on October 19th, Radar
Networks will announce a revolutionary new service that uses the power
of the emerging Semantic Web to enable a smarter way of sharing,
organizing and finding information. Founder and CEO Nova Spivack will
also give the first public preview of Radar’s application, which is one
of the first examples of “Web 3.0” – the next-generation of the Web, in
which the Web begins to function more like a database, and software
grows more intelligent and helpful.

Join Nova as he participates in “The Semantic Edge” panel discussion
with esteemed colleagues including Powerset’s Barney Pell and Metaweb’s
Daniel Hillis, moderated by Tim O’Reilly.

Who:   
Radar Networks Founder and CEO Nova Spivack

When:   
Friday, October 19, 2007
4:10 – 4:55 p.m.
   
Where: 
Web2.0 Summit
Palace Hotel
Grand Ballroom
2 New Montgomery Street
San Francisco,  California  94105