%0 
%0 Conference Proceedings
%A Borgida, Alexander; Walsh, Thomas & Hirsh, Haym
%D 2005
%T Towards Measuring Similarity in Description Logics.
%E Horrocks, Ian; Sattler, Ulrike & Wolter, Frank
%B Proceedings of the 2005 International Workshop on Description Logics (DL2005), July 26-28, 2005, Edinburgh, Scotland, UK
%C 
%I CEUR-WS.org
%V 147
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%F borgida05similarity
%K descriptionlogic master matchmaking 
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%U http://www.ceur-ws.org/Vol-147/25-BorgidaEtAl.pdf
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%0 
%0 Journal Article
%A Jiang, Jay J. & Conrath, David W.
%D 1997
%T Semantic Similarity Based on Corpus Statistics and Lexical Taxonomy
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%B CoRR
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%V cmp-lg/9709008
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%K master matchmaking similarity 
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%Z informal publication
%U http://dblp.uni-trier.de/db/journals/corr/corr9709.html#cmp-lg-9709008
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%0 
%0 Conference Proceedings
%A Kiefer, Christoph; Bernstein, Abraham & Stocker, Markus
%D 2007
%T The Fundamentals of iSPARQL - A Virtual Triple Approach For Similarity-Based Semantic Web Tasks
%E Aberer, Karl; Choi, Key-Sun; Noy, Natasha; Allemang, Dean; Lee, Kyung-Il; Nixon, Lyndon J B; Golbeck, Jennifer; Mika, Peter; Maynard, Diana; Schreiber, Guus & Cudré-Mauroux, Philippe
%B Proceedings of the 6th International Semantic Web Conference and 2nd Asian Semantic Web Conference (ISWC/ASWC2007), Busan, South Korea
%C Berlin, Heidelberg
%I Springer Verlag
%V 4825
%6
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%P 295--308
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%S LNCS
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%8 November
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%4 http://data.semanticweb.org/conference/iswc-aswc/2007/proceedings
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%F Kiefer/2007/Fundamentals
%K master matchmaking sparql 
%X This research explores three SPARQL-based techniques to solve Semantic Web tasks that often require similarity measures, such as semantic data integration, ontology mapping, and Semantic Web service matchmaking. Our aim is to see how far it is possible to integrate customized similarity functions (CSF) into SPARQL to achieve good results for these tasks. Our first approach exploits virtual triples calling property functions to establish virtual relations among resources under comparison; the second approach uses extension functions to filter out resources that do not meet the requested similarity criteria; finally, our third technique applies new solution modifiers to post-process a SPARQL solution sequence. The semantics of the three approaches are formally elaborated and discussed. We close the paper with a demonstration of the usefulness of our iSPARQL framework in the context of a data integration and an ontology mapping experiment.
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%U http://iswc2007.semanticweb.org/papers/295.pdf
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%0 
%0 Conference Proceedings
%A Klusch, Matthias; Fries, Benedikt & Sycara, Katia
%D 2006
%T Automated semantic web service discovery with OWLS-MX.
%E Nakashima, Hideyuki; Wellman, Michael P.; Weiss, Gerhard & Stone, Peter
%B AAMAS
%C 
%I ACM
%V 
%6
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%P 915-922
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%4 conf/atal/2006
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%F conf/atal/KluschFS06
%K descriptionlogic master matchmaking 
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%U http://dblp.uni-trier.de/db/conf/atal/aamas2006.html#KluschFS06
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%0 
%0 Journal Article
%A Leacock, Claudia; Chodorow, Martin & Miller, George A.
%D 1998
%T Using Corpus Statistics and WordNet Relations for Sense Identification
%E 
%B Computational Linguistics
%C 
%I 
%V 24
%6
%N 1
%P 147-165
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%K master matchmaking similarity 
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%U citeseer.ist.psu.edu/leacock98using.html
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%0 
%0 Conference Proceedings
%A Lin, Dekang
%D 1998
%T An Information-Theoretic Definition of Similarity
%E Shavlik, Jude W.
%B Proceedings of the Fifteenth International Conference on Machine Learning (ICML 1998), Madison, Wisconson, USA, July 24-27, 1998
%C 
%I Morgan Kaufmann
%V 
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%P 296-304
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%F lin98informationtheroreticsimilarity
%K master matchmaking similarity 
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%U http://dblp.uni-trier.de/db/conf/icml/icml1998.html#Lin98
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%^

%0 
%0 Journal Article
%A Linden, G.; Smith, B. & York, J.
%D 2003
%T Amazon.com recommendations: item-to-item collaborative filtering
%E 
%B Internet Computing, IEEE
%C 
%I 
%V 7
%6
%N 1
%P 76--80
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%F linden2003
%K master matchmaking recommender 
%X Recommendation algorithms are best known for their use on e-commerce Web sites, where they use input about a customer's interests to generate a list of recommended items. Many applications use only the items that customers purchase and explicitly rate to represent their interests, but they can also use other attributes, including items viewed, demographic data, subject interests, and favorite artists. At Amazon.com, we use recommendation algorithms to personalize the online store for each customer. The store radically changes based on customer interests, showing programming titles to a software engineer and baby toys to a new mother. There are three common approaches to solving the recommendation problem: traditional collaborative filtering, cluster models, and search-based methods. Here, we compare these methods with our algorithm, which we call item-to-item collaborative filtering. Unlike traditional collaborative filtering, our algorithm's online computation scales independently of the number of customers and number of items in the product catalog. Our algorithm produces recommendations in real-time, scales to massive data sets, and generates high quality recommendations.
%Z 
%U http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1167344
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%0 
%0 Generic
%A Resnik, Philip
%D 1995
%T Using Information Content to Evaluate Semantic Similarity in a Taxonomy
%E
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%1 resnik95-using.pdf
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%F resnik95-using
%K master matchmaking similarity 
%X This paper presents a new measure of semantic similarity in an IS-A taxonomy, based on the notion of information content. Experimental evaluation suggests that the measure performs encouragingly well (a correlation of r = 0.79 with a benchmark set of human similarity judgments, with an upper bound of r = 0.90 for human subjects performing the same task), and significantly better than the traditional edge counting approach (r = 0.66).
%Z 
%U http://www.citebase.org/abstract?id=oai:arXiv.org:cmp-lg/9511007
%+
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%0 
%0 Conference Proceedings
%A Sarwar, Badrul; Karypis, George; Konstan, Joseph & Riedl, John
%D 2000
%T Analysis of recommendation algorithms for e-commerce
%E 
%B EC '00: Proceedings of the 2nd ACM conference on Electronic commerce
%C New York, NY, USA
%I ACM Press
%V 
%6
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%P 158--167
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%@ 1-58113-272-7
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%F 352887
%K master matchmaking recommender 
%X 
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%U http://portal.acm.org/citation.cfm?id=352887
%+
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%0 
%0 Conference Proceedings
%A Wu, Zhibiao & Palmer, Martha Stone
%D 1994
%T Verb Semantics and Lexical Selection
%E 
%B Proceedings of the 32nd. Annual Meeting of the Association for Computational Linguistics (ACL 1994)
%C 
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%P 133-138
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%K master matchmaking similarity 
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%U http://dblp.uni-trier.de/db/conf/acl/acl94.html#WuP94
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