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The Creation and Evaluation of iSPARQL Strategies for Matchmaking

by: Christoph Kiefer, and Abraham Bernstein

In: Proceedings of the 5th European Semantic Web Conference Berlin, Heidelberg: Springer Verlag (June 2008) .

Resources (URL, PDF, PS...)

URL:	http://data.semanticweb.org/conference/eswc/2008/papers/133

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.

BibTeX record

@inproceedings{kiefer2008creation,
  abstract = {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.},
  added-at = {2008-05-28T14:49:57.000+0200},
  address = {Berlin, Heidelberg},
  author = {Kiefer, Christoph and Bernstein, Abraham},
  biburl = {http://www.bibsonomy.org/bibtex/24058a55f996d8c24ef69c32f3c81ccdd/eswc2008},
  booktitle = {Proceedings of the 5th European Semantic Web Conference},
  editor = {Hauswirth, Manfred and Koubarakis, Manolis and Bechhofer, Sean},
  interhash = {88ebc745190a2df9e0990f15ea2cdda7},
  intrahash = {4058a55f996d8c24ef69c32f3c81ccdd},
  keywords = {retrieval information matchmaking machine learning sparql evaluation query-processing-1},
  month = {June},
  publisher = {Springer Verlag},
  series = {LNCS},
  timestamp = {2008-05-28T14:49:57.000+0200},
  title = {The Creation and Evaluation of iSPARQL Strategies for Matchmaking},
  url = {http://data.semanticweb.org/conference/eswc/2008/papers/133},
  year = 2008
}

Endnote record

%0 Conference Paper
%1 kiefer2008creation
%A Kiefer, Christoph
%A Bernstein, Abraham
%B Proceedings of the 5th European Semantic Web Conference
%C Berlin, Heidelberg
%D 2008
%E Hauswirth, Manfred
%E Koubarakis, Manolis
%E Bechhofer, Sean
%I Springer Verlag
%K 
%T The Creation and Evaluation of iSPARQL Strategies for Matchmaking
%U http://data.semanticweb.org/conference/eswc/2008/papers/133
%X 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.

BibSonomy :: publication ::

The Creation and Evaluation of iSPARQL Strategies for Matchmaking

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Abstract

BibTeX record

Endnote record