In her quest to bring you the most authentic, up-to-date news about the evolution of the web, this reporter is venturing where few go: straight to the heart of NYC’s little-known Semantic Web community. It is there, buried in rule interchange formats and Unicode, hidden behind coke-bottle glasses and tablet PCs, that she hopes to find the people who are actually building the web.
Last night was my second New York Semantic Web Meetup event, so I knew a little more about what to expect (free pizza and liberal use of PowerPoint, unusually high Y to X chromosome ratio). The night was divided between two speakers: Mike Cataldo (CEO of Cambridge Semantics, which uses semantic web technology to solve businesses’ problems) and Lee Feigenbaum (a VP at Cambridge Semantics and co-chair of W3C‘s SPARQL working group – which I’ll explain later). It alternated between pretty heavy business-talk (“…and that’s game-changing!”) and tech-talk (“Supplant the mystifying OPTIONAL/!bound method of negation with a dedicated construct), but here’s what I was able to scrape together:
So Cambridge Semantics provides “practical solutions for today’s business problems using the most advanced semantic technology” – what does that mean? Essentially, they make it easier to get the data a company needs out of the applications that keep it locked up. A cool feature is that they have a plug-in to use Microsoft Excel as both a source for the data and as an interface for looking at it.
Apparently, there are a couple companies using Cambridge Semantics technology now, including a biopharmaceutical firm in Belgium and a startup called Book Of Odds that calculates the odds of various everyday activities.
As Lee Feigenbaum told me later, if SPARQL is working the way it should, most people shouldn’t even know that it’s there. That said, it’s probably useful to know a little about this core Semantic Web technology, if only to get a better idea of what the Semantic Web might be capable of.
SPARQL is a query language – it’s built for asking questions (and getting back the right answers). It’s got to be able to ask questions that pull together data from a lot of different sources in new, complex ways. The example query Feigenbaum gave in his talk was: “What are the names of all landlocked countries with a population greater than 15 million?” To answer that question, SPARQL first has to know about words like “country” and “population” (that “population” is a property of “country,” for example, and that “population” should refer to a number) and then combine information from different databases to get the right answer. What SPARQL does, then, is a whole lot more powerful than what Google does (Google just matches words in your question to popular pages where the same words show up). Try typing the example question into Google: when I did, the first hit was an entreaty for the world to help “the landlocked heart of Africa” and the second was actually a reference to Feigenbaum’s lecture. I’d tell you how long it took to get the right answer, except that I got sick of looking through the irrelevant documents somewhere on the sixth page of results.
So it’s easy to see how SPARQL can be a great piece of technology. It’s also easy to see why SPARQL is a semantic web technology – it can only come up with answers if the information it’s looking at is written in a computer-understandable language – RDF in this case. One of the main things that gets people excited about the Semantic Web is it’s question-answering ability, and SPARQL is what’s going to make that possible.*
*Note: actually what Feigenbaum was talking about last night was SPARQL 2 – the next version of SPARQL that he’s helping to develop at the W3C. In the interests of space and your waning interest, I’m not going to outline the differences between SPARQL and SPARQL 2 – if you’re really concerned about it, take a look at Feigenbaum’s presentation slides yourself.