Semantic Web Dog Food

Presented at: 5th European Semantic Web Conference (ESWC2008)

by Christoph Kiefer, Abraham Bernstein

This research explores our novel method for Semantic Web service matchmaking based on iSPARQL queries, which enable the user to query the Semantic Web with techniques from traditional information retrieval. The strategies for matchmaking which we develop and evaluate in the paper make use of a plethora of similarity measures and combination functions from SimPack -- our library of similarity measures for the use in ontologies. We show how our combination of structured and imprecise querying can be used to perform hybrid Semantic Web service matchmaking in simple and amazingly fast fashion. We analyze our approach thoroughly on a large OWL-S service test collection, and show how our initial strategies can be improved by applying machine learning algorithms such as regression, decision trees, or support vector machines to result in the most effective strategies for matchmaking.

Keywords: evaluation, information retrieval, machine learning, matchmaking, sparql, Machine Learning, SPARQL, Search Engine, Semantic Web, Semantic Web Services

Resource URI on the dog food server: http://data.semanticweb.org/conference/eswc/2008/paper/133
Same as: http://revyu.com/things/eswc-2008-paper-the-creation-matchmaking
Same as: http://semanticweb.org/id/The_Creation_and_Evaluation_of_iSPARQL_Strategies_for_Matchmaking

Explore this resource elsewhere:

• on Snorql
• on VisiNav
• on Sindice