The Creation and Evaluation of iSPARQL Strategies for Matchmaking

This research explores a new method for Semantic Web service matchmaking based on iSPARQL strategies, which enables to query the Semantic Web with techniques from traditional information retrieval. The strategies for matchmaking that we developed and evaluated can make use of a plethora of similarity measures and combination functions from SimPack--our library of similarity measures. We show how our combination of structured and imprecise querying can be used to perform hybrid Semantic Web service matchmaking. 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 to result in very effective strategies for matchmaking.

[1]  William E. Winkler,et al.  AN APPLICATION OF THE FELLEGI-SUNTER MODEL OF RECORD LINKAGE TO THE 1990 U.S. DECENNIAL CENSUS , 1987 .

[2]  Enrico Motta,et al.  The Semantic Web - ISWC 2005, 4th International Semantic Web Conference, ISWC 2005, Galway, Ireland, November 6-10, 2005, Proceedings , 2005, SEMWEB.

[3]  Tom Fawcett,et al.  Robust Classification for Imprecise Environments , 2000, Machine Learning.

[4]  Deborah L. McGuinness,et al.  CLASSIC: a structural data model for objects , 1989, SIGMOD '89.

[5]  Kurt Geihs,et al.  Ranked Matching for Service Descriptions Using OWL-S , 2004, KiVS.

[6]  Catherine Faron-Zucker,et al.  Searching the semantic Web: approximate query processing based on ontologies , 2006, IEEE Intelligent Systems.

[7]  Francesco M. Donini,et al.  A system for principled matchmaking in an electronic marketplace , 2003, WWW '03.

[8]  Mark Klein,et al.  Semantic Process Retrieval with iSPARQL , 2007, ESWC.

[9]  Vijay V. Raghavan,et al.  Retrieval system evaluation using recall and precision: problems and answers , 1989, SIGIR '89.

[10]  Stefanos D. Kollias,et al.  A String Metric for Ontology Alignment , 2005, SEMWEB.

[11]  Jie Zhang,et al.  Semplore: An IR Approach to Scalable Hybrid Query of Semantic Web Data , 2007, ISWC/ASWC.

[12]  Matthias Klusch,et al.  Dynamic service matchmaking among agents in open information environments , 1999, SGMD.

[13]  Fabrizio Sebastiani,et al.  Machine learning in automated text categorization , 2001, CSUR.

[14]  Matthias Klusch,et al.  Automated semantic web service discovery with OWLS-MX , 2006, AAMAS '06.

[15]  Abraham Bernstein,et al.  The Fundamentals of iSPARQL: A Virtual Triple Approach for Similarity-Based Semantic Web Tasks , 2007, ISWC/ASWC.

[16]  Valeria De Antonellis,et al.  Semantic-Enriched Service Discovery , 2006, 22nd International Conference on Data Engineering Workshops (ICDEW'06).

[17]  Jérôme Euzenat,et al.  Ontology Alignment with OLA , 2004, EON.

[18]  Dave Reynolds,et al.  SPARQL basic graph pattern optimization using selectivity estimation , 2008, WWW.

[19]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .

[20]  L. R. Dice.  Measures of the Amount of Ecologic Association Between Species , 1945 .

[21]  Tao Qin,et al.  N -Step PageRank for Web Search , 2007, ECIR.

[22]  Abraham Bernstein,et al.  OptARQ: A SPARQL Optimization Approach based on Triple Pattern Selectivity Estimation , 2007 .

[23]  James A. Hendler,et al.  The Semantic Web — ISWC 2002 , 2002, Lecture Notes in Computer Science.

[24]  Pradeep Ravikumar,et al.  A Comparison of String Distance Metrics for Name-Matching Tasks , 2003, IIWeb.

[25]  Ian Witten,et al.  Data Mining , 2000 .

[26]  Takahiro Kawamura,et al.  Semantic Matching of Web Services Capabilities , 2002, SEMWEB.

[27]  Chih-Jen Lin,et al.  A Practical Guide to Support Vector Classication , 2008 .

[28]  David D. Lewis,et al.  Evaluating Text Categorization I , 1991, HLT.

[29]  Lora Aroyo,et al.  The Semantic Web: Research and Applications , 2009, Lecture Notes in Computer Science.

[30]  Vladimir I. Levenshtein,et al.  Binary codes capable of correcting deletions, insertions, and reversals , 1965 .