Nova Spivack - Minding the Planet » The Semantic Graph

What's After the Real Time Web?

Nova — Wed, 28 Oct 2009 04:08:01 +0000

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:

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.
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 — Search 3.0

Nova — Sat, 23 May 2009 06:26:15 +0000

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.

Twine's Explosive Growth

Nova — Fri, 06 Feb 2009 19:05:38 +0000

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.

Fast Company Interview — "Connective Intelligence"

Nova — Mon, 08 Dec 2008 20:50:18 +0000

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.

Interest Networks are at a Tipping Point

Nova — Mon, 20 Oct 2008 22:01:45 +0000

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:

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!

Watch My best Talk: The Global Brain is Coming

Nova — Thu, 02 Oct 2008 19:56:57 +0000

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!

The Future of the Desktop

Nova — Sun, 27 Jul 2008 01:14:49 +0000

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

—————

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.

Video of my Presentation at The Next Web 2008 Conference

Nova — Tue, 03 Jun 2008 15:39:13 +0000

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!

Tagging and the Semantic Web: Tags as Objects

Nova — Thu, 22 May 2008 08:02:10 +0000

John Mills, one of the engineers behind Twine, recently wrote up an interesting article discussing our approach to semantic tags. It’s a good read for folks who think about the Semantic Web and tags.

emanti

Associative Search and the Semantic Web: The Next Step Beyond Natural Language Search

Nova — Tue, 13 May 2008 18:31:08 +0000

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.

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.
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.

Cool Twine Fan Video by a High-School Student

Nova — Tue, 15 Apr 2008 23:55:43 +0000

I was pleasantly surprised to see a very nice fan video for Twine created by a high-school student who is in our beta test. It gives the flavor of Twine and is really nice.

A Few Predictions for the Near Future

Nova — Sat, 12 Apr 2008 10:44:37 +0000

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.

My Visit to DERI — World's Premier Semantic Web Research Institute

Nova — Wed, 26 Mar 2008 17:27:21 +0000

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.

Insightful Article About Twine

Nova — Thu, 06 Mar 2008 10:34:05 +0000

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

How about Web 3G?

Nova — Tue, 04 Mar 2008 07:14:35 +0000

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.

A Nice Video Intro to The Semantic Web for Non-Geeks

Nova — Tue, 15 Jan 2008 02:41:19 +0000

Question: What do you do if you’re not a computer scientist but you are interested in understanding what all this Semantic Web stuff is about?

Answer: Watch this video!

Help the Semantic Web Win the Crunchies

Nova — Thu, 03 Jan 2008 18:44:09 +0000

As I blogged earlier, my company’s product, Twine.com, has been nominated as a finalist for the Best Technology Achievement of 2007 in the TechCrunch Crunchies awards. You can vote once per day, per browser, it appears (they have a very scientific voting process it seems), so please cast your vote for Twine (and the Semantic Web) by voting every day until January 10 when voting ends. You can vote here. Thanks for helping to support the Semantic Web!

Help us Win! Twine is a Finalist in the Crunchies!

Nova — Wed, 26 Dec 2007 17:11:44 +0000

My company’s product, Twine.com, has made it to the finalist round in the Crunchies, a new annual tech industry awards competition, under the Best Technical Achievement category. Please help us win by casting your vote for Twine here. Thanks!

UPDATE: It turns out, that for some odd reason the Crunchies allows each voter to vote once per day per category — in other words, you can vote multiple times in the same category — one vote per user per day — so please vote for Twine again if you can.

First Full Online Demo of Twine

Nova — Thu, 13 Dec 2007 16:29:20 +0000

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.

Defining the Semantic Graph — What is it Really?

Nova — Sat, 24 Nov 2007 00:30:43 +0000

This is written in response to a post by Anne Zelenka.

I’ve been talking about the coming “semantic graph” for quite some time now, and it seems the meme has suddenly caught on thanks to a recent article by Tim Berners-Lee in which he speaks of an emerging “Giant Global Graph” or “GGG.” But if the GGG emerges it may or may not be semantic. For example social networks are NOT semantic today, even though they contain various kinds of links between people and other things.

So what makes a graph “semantic?” How is the semantic graph different from social networks like Facebook for example?

Many people think that the difference between a social graph and a semantic graph is that a semantic graph contains more types of nodes and links. That’s potentially true, but not always the case. In fact, you can make a semantic social graph or a non-semantic social graph. The concept of whether a graph is semantic is orthogonal to whether it is social.

A graph is “semantic” if the meaning of the graph is definedand exposed in an open and machine-understandable fashion. In otherwords, a graph is semantic if the semantics of the graph are part ofthe graph or atleast connected from the graph. This can be accomplished by representing a social graph using RDF and OWL, the languages of the Semantic Web.

Today most social networks are non-semantic, but it is relatively easy to transform them into semantic graphs. A simple way to make any non-semantic social graph into a semantic social graph is touse the FOAF ontology to define the entities and links in the graph.

FOAF stands for “friend of a friend” and is a simple ontology of peopleand social relationships. If a social network links its data to theFOAF ontology, and exposes these linkages to other applications on theWeb, then other applications can understand the meaning of the data inthe network in an unambiguous manner. In other words it is now asemantic social graph because its semantics are visible to otherapplications.

As illustrated by the FOAF example above, one way to make a graphsemantic is to use the W3C open standards for the Semantic Web (RDF andOWL) to represent, and define the meaning of, the nodes and links inthe graph. By using the Semantic Web, the graph becomesmachine-understandable and thus more easily navigated, imported by,searched, and integrated by other applications.

For example, let’s say that social network Application A comes alongand wants to use the dataset of social network Application B. App Asees the graph of nodes and links in B, and it sees something called a”has team” link connecting various nodes in the graph together. Whatdoes that mean? What kinds of things can or cannot be connected withthis link? What can be inferred if things are connected this way?

The meaning of “has team” is ambiguous to App A because it’s notdefined anywhere that the software can see. The only way App A can useApp B’s data correctly is if the programmer of App A speaks to theprogrammer of App B (or reads something they wrote such asdocumentation of some sort) that defines what they meant by the “hasteam” link.

Only by knowing what was intended by the programmer of App B, canApp A treat App B’s data appropriately, without any misinterpretationthat might lead to mistakes or inconsistencies. This is importantbecause, for example, if a user searches for “Yankees Players” shouldpeople who are linked by the “has team” link to sports teams called”Yankees” be returned, or does “has team” mean “a connection from aperson to a sports team they support,” or does it mean “a connectionfrom a person to a sports team they play on,” or does it mean “aconnection from a person to a workgroup they participate in?” In short,App A has no idea what to do with data that is linked by App B’s “hasteam” link unless it is explicitly programmed to make use of it.

The OWL language (Web Ontology Language) provides a way for theprogrammers of App A and App B to define what the links in their graphsmean in an unambiguous and machine-understandable way. So App A justhas to look up this definition and it can instantly start to use AppB’s data correctly, without any new programming or difficultintegration.

How is this accomplished? The programmer of App B simply uses OWL todefine an ontology of social relationships for their service: forexample they define the “has team” link to be a link that connects aperson to a sports team they play on. They also define what they meanby a “sports team” (for example, “a group of two or more people thatplay a sport” and a sport is one of “baseball, basketball, football,soccer, hockey, tennis” and they link these terms to another ontologyof sports somewhere else on the Web.) The ontology file that definesApp B’s data is added to the Website of App B, and linked from it’sdata, so that other applications can see it.

Now when another application such as App A comes along and looks atApp B’s data it can reference App B’s ontology to see for itself whatwas intended by the “has team” link — it can see exactly what thatlink implies and what can be inferred by it. It understands how to useApp B’s data set, and how to correctly make new links using that dataset which are consistent with the meaning of the links it contains.

This is the real point of the Semantic Web open standards — RDFenables data to be represented in a database independent manner, andOWL enables the semantic of that data to be defined in an openmachine-understandable way so that other applications can use that datawithout having to first be programmed to do so. As long as they speakRDF/OWL, applications can use any data they find and lookup the meaningof any data they need to use so they can use the data appropriately.

For example, suppose another application, App C, that is OWL-awareapplication but has never seen App B’s data-set before and was notprogrammed specifically to use it, pulls some data out from App B’sAPI. App C can immediately begin to use this data correctly andconsistently with how App B uses it, because all that is necessary forunderstanding how to use B’s data is encoded in the OWL ontology thatApp B’s data refers to.

The point is here that using Semantic Web open standards such as RDFand OWL to encode what data means is a giant leap beyond just puttingraw data onto the Web in an open format. It doesn’t just put the dataitself on the Web, it also puts the definition of what the data meansand how to use it, on the Web in an open format. A semantic graph isfar more reusable than a non-semantic graph — it’s a graph thatcarries its own meaning.

The semantic graph is not merely a graph with links to more kinds ofthings than the social graph. It’s a graph of interconnected thingsthat is machine-understandable — it’s meaning or “semantics” isexplicitly represented on the Web, just like its data. This is the realway to make social networks open. Merely opening up their API’s is justthe first step.

Only when the semantics of data is defined and shared in an open waycan any graph truly be said to be semantic. Once data around the Web isdefined in a machine-understandable way, a whole new world of easy,instant mashups becomes possible. Applications can start to freely andinstantly mix and match each other’s data, including new data they werenot programmed in advance to understand. This opens up the door to theWeb truly becoming a giant database and eventually an integratedoperating system in which all applications are able to more easilyinteroperate and share data.

The Giant Global Graph may or may not be a semantic graph. Thatdepends on whether it is implemented with, or at least connected to,W3C standards for the Semantic Web.

I believe that because the Semantic Web makes data-integrationeasier, it will ultimately be widely adopted. Simply put, applicationsthat wish to access or integrate data in the Age of the Web can moreeasily do so using RDF and OWL. That alone is reason enough to usethese standards.

Of course there are many other benefits as well, such as the abilityto do more sophisticated reasoning across the data, but that is lessimportant. Simply making data more accessible, connectable, andreusable across applications would be a huge benefit.

Powerpoint Deck: Making Sense of the Semantic Web, and Twine

Nova — Wed, 21 Nov 2007 08:13:56 +0000

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.

Quick Video Preview of Twine

Nova — Sat, 10 Nov 2007 00:15:35 +0000

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:

True Knowledge is Cool

Nova — Thu, 08 Nov 2007 00:54:46 +0000

The most interesting and exciting new app I’ve seen this month (other than Twine of course!) is a new semantic search engine called True Knowledge. Go to their site and watch their screencast to see what the next generation of search is really going to look like.

True Knowledge is doing something very different from Twine — whereas Twine is about helping individuals, groups and teams manage their private and shared knowledge, True Knowledge is about making a better public knowledgebase on the Web — in a sense they are a better search engine combined with a better Wikipedia. They seem to overlap more with what is being done by natural language search companies like Powerset and companies working on public databases, such as Metaweb and Wikia.

I don’t yet know whether True Knowledge is supporting W3C open-standards for the Semantic Web, but if they do, they will be well-positioned to become a very central service in the next phase of the Web. If they don’t they will just be yet another silo of data — but a very useful one at least. I personally hope they provide SPARQL API access at the very least. Congratulations to the team at True Knowledge! This is a very impressive piece of work.

The Next Big Thing: User-Contributed Metadata

Nova — Mon, 29 Oct 2007 15:56:08 +0000

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.

A Video and an Audio Cast About Twine

Nova — Thu, 25 Oct 2007 16:13:21 +0000

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!

Nova — Sat, 20 Oct 2007 08:09:53 +0000

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!!!