The semantic web is upon us, says Berners-Lee

The semantic web is upon us, says Berners-Lee - WebWatch: "The semantic web, where machines are able to read the contents of documents as readily as people can, now has all the standards and technologies it needs to succeed, according to W3C director Sir Tim Berners-Lee. Speaking at the WWW2006 conference in Edinburgh on Wednesday, he said it was now time for web developers and content producers to start using semantic languages in addition to HTML."

The Semantic Web Revisited

ECS EPrints Service - The Semantic Web Revisited: "The original Scientific American article on the Semantic Web appeared in 2001. It described the evolution of a Web that consisted largely of documents for humans to read to one that included data and information for computers to manipulate. The Semantic Web is a Web of actionable information--information derived from data through a semantic theory for interpreting the symbols. This simple idea, however, remains largely unrealized. Shopbots and auction bots abound on the Web, but these are essentially handcrafted for particular tasks; they have little ability to interact with heterogeneous data and information types. Because we haven't yet delivered large-scale, agent-based mediation, some commentators argue that the Semantic Web has failed to deliver. We argue that agents can only flourish when standards are well established and that the Web standards for expressing shared meaning have progressed steadily over the past five years. Furthermore, we see the use of ontologies in the e-science community presaging ultimate success for the Semantic Web--just as the use of HTTP within the CERN particle physics community led to the revolutionary success of the original Web. This article is part of a special issue on the Future of AI."

Content-based Ontology Ranking

ECS EPrints Service - Content-based Ontology Ranking: "Techniques to rank ontologies are crucial to aid and encourage the re-use of publicly available ontologies. This paper presents a system that obtains a list of ontologies from a search engine that contain the terms provided by a knowledge engineer and ranks them. The ranking of these ontologies will be done according to how many of the concept labels in those ontologies match a set of terms extracted from a corpus of documents related to the domain of knowledge identified by the knowledge engineer's original search terms."