Swangling S S Inference U C B M        

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Presentation transcript:

Swangling S S Inference U C B M         Convincing Internet Search Engines to Index Semantic Markup my.owl.doc my.swangled.doc OWL Document <A, B, C> <A, E, C> etc. The Swangler converts each triple of the document to a single indexing term Swangled Document <A, B, C> <A, E, C> etc. 5V4F56A5XJ LA846CM7RR  The resulting document contains both semantic markup and swangled terms  S An original document contains semantic markup  The Web server sends the swangled page to Internet search engine spiders  The search engine indexes the swangled document under the swangled terms  5V4F56A5XJ: my.swangled.doc another.doc etc. The search engine returns a reference to the swangled document  5V4F56A5XJ Inference Search for semantic markup is marginally useful if only exact matches are allowed. The Swangler allows forward-chaining inference to add arbitrary new triples to a document. For example, it can replace the elements of each triple with wildcards, to form six new triples: <*, B, C> <A, *, C> <A, B, *> <*, *, C> <*, B, *> <A, *, *> Now a search for the swangled version of <A, *, C> for example will find all documents that relate A and C in the desired way. <A, B, C> The Swangler converts the user’s request to swangled terms  my.swangled.doc S <A, B, C> The user requests desired semantic markup  Get the Swangler at http://projects.semwebcentral.org/projects/swangle/ U C B M Partial research support was provided by DARPA contract F30602-00-0591

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