Slashdot Mirror
Going from a 'Web of links' to a 'Web of meaning'
neutron_p writes "Computer scientists from Lehigh University are building the Semantic Web, which will handle more data, resolve contradictions and draw inferences from users' queries. The new improved Web will also combine pieces of information from multiple sites in order to find answers to questions."
Covered not long ago - an interview with Berners-Lee regarding the Semantic Web.
People at DERI in Ireland's Galway are also working on the Semantic Web (see http://www.deri.ie/). I thought lots of people are...
The semantic web is a pretty popular area of research right now and its far from being "built by computer scientists at Lehigh University", in fact I could have done an undergrad dissertation on the semantic web, and there were numerous phD positions being advertised at uni's around the world researching about the semantic web.
Whichever lehigh uni professor submitted this is stooping pretty low trying to raise publicity (and hence finance) I would think!
I have discovered a truly remarkable sig which this post is too small to contain.
The Semantic Web's use to resolve contradictions is probably least applied, at least in these early stages. Also, it is not meant to be a global information store (in which all contradictions may be resolved). It is meant to be large numbers of globally connected information stores, and between small numbers of these contradictions may be resolved.
Also, the ontology of the semantic web comes in 3 flavors, OWL Lite, OWL DL, and OWL Full. The first two are limited enough that they are decidable (I'm not sure if this is guaranteed or just true for most use cases). OWL Lite in particular is light weight enough that processing of it is in reach for data stores, but powerful enough far more information can be inferred than what is directly stated in the RDF.
For to end yet again.
There's absolutely loads of it around... especially as people are starting to use more generated websites (like slashdot for example).
If you search for *.rdf maybe you won't find as much... a lot of it is *.rss, *.xml and other things.
Also, google doesn't index them.
A message has "meaning" if you can make special use of it.
Normal web pages have meaning for browsers, it's just that that meaning is limited to "how to draw words for the user."
What we're doing, is making it so that your computer can make special use of messages on the web, to do smarter things.
It would be scary if the Semantic Web were about "my meaning is THE meaning." But it is explicitely not like that. In fact, one of the main things about it is that anyone can make up their own languages, their own way of modelling the world.
There are tools that make it so you can say, "My word X is sort of like their word Y," but it's acknowledged that such translations will be imperfect. Likely, fuzzy logic, and systems that are able to ask for clarification (and remember responses), will be used to mediate that sort of things.
You may also be interested in my favorite page on AI by Open Mind. The Semantic Web isn't explicitely about AI, but it opens the door for a lot of AI work.
Google works because it is largely a statistical tool that uses some meta-information.
While I could see frameworks being used for very specific purposes, like searching a homogeneous (e.g., slashdot, pubmed, nytimes) web-site where all content is controlled. But extending these ideas to a heterogenous web that would no doubt take advantages of such a volunteer system is ludicrous.
I also take issue with the top-down mind-state that they will be able to predict what is useful to the user. This is why statistical importance and quantity is the only realistic method for such a massive undertaking (which google is still actively researching).
I think that the only useful research to come out of such an endeavor would be to have news-sites, as mentioned above, implement and be scanned using an ontological browser. Of course, I am not sure how this would be different than Lexus-Nexus (sp?).
There has been a considerable amount of work on ontology mapping within the knowledge engineering community, but the evolutionary aspects of ontologies have been largely overlooked. Ontology mapping is a harder problem than graph isomorphism, since classes from different ontologies may have extensions that overlap rather than cover each other. It's a difficult problem, certainly, but it's worth noting that game theory isn't applied here.
Game theory tends to appear more within the multi-agent systems community than the semantic web community; they've been looking at the social models for trust for some years now.