Abstract
This chapter presents the methodological and technical approach, as well as evaluation results, for two semantic matchmakers, COV4SWS.KOM and LOG4SWS.KOM. Both matchmakers operate on WSDL-based service description with SAWSDL annotations. COV4SWS.KOM applies similarity measures from the field of semantic relatedness, namely the metrics by Lin and Resnik. It automatically adapts to varying expressiveness of a service description on different abstraction levels through the utilization of an Ordinary Least Squares (OLS) estimator. LOG4SWS.KOM employs traditional subsumption reasoning, but maps the resulting discrete Degrees of Match (DoMs) to numerical equivalents to allow for the integration with additional similarity measures. As proof of concept, a path length-based measure is applied. The DoM mapping process may either be conducted manually or using an OLS estimator. Both matchmakers participated in the Semantic Service Selection (S3) Contest in 2010, providing very competitive evaluation results across all regarded performance metrics.
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Notes
- 1.
The names of our matchmakers have historical roots: COV was traditionally based on the determination of the degree of coverage between semantic concepts; LOG refers to logic subsumption matching. The common name component 4SWS means “for Semantic Web Services”, KOM refers to the abbreviated name of our institute at Technische Universität Darmstadt.
- 2.
As a matter of fact, both matchmakers are also applicable to service description formalisms that exhibit a structure similar to (SA)WSDL. An application of LOG4SWS.KOM to hRESTS with MicroWSMO annotations – service description formalisms for RESTful services – has been presented by Lampe et al. [12].
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Both test collections are available at http://projects.semwebcentral.org/projects/sawsdl-tc/
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Using the mean average of each metric across the comparable Variants 1A–3B as a basis for comparison.
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References
U. Bellur, R. Kulkarni, Improved matchmaking algorithm for semantic web services based on bipartite graph matching, in 2007 IEEE International Conference on Web Services, Hong Kong, 2007, pp. 86–93
F. Bourgeois, J.-C. Lassalle, An extension of the Munkres algorithm for the assignment problem to rectangular matrices. Commun. ACM 14(12), 802–804 (1971)
A. Budanitsky, G. Hirst, Evaluating wordNet-based measures of lexical semantic relatedness. Comput. Linguist. 32(1), 13–47 (2006)
J. Cardoso, Discovering semantic web services with and without a common ontology commitment, in Third International Semantic and Dynamic Web Processes Workshop, Chicago, 2006, pp. 183–190
A. Fernández, A. Polleres, S. Ossowski, Towards fine-grained service matchmaking by using concept similarity, in First International Joint Workshop SMR 2 2007 on Service Matchmaking and Resource Retrieval in the Semantic Web at the 6th International Semantic Web Conference, vol. 243, Busan, 2007, pp. 31–46
R. Guo, D. Chen, J. Le, Matching semantic web services across hetero-geneous ontologies, in Fifth International Conference on Computer and Information Technology, Shanghai, 2005, pp. 264–268
M. Klusch, P. Kapahnke, I. Zinnikus, Hybrid daptive web service selection with SAWSDL-MX and WSDL-Analyzer, in The Semantic Web: Research and Applications, ed. by L. Aroyo, P. Traverso, F. Ciravegna, P. Cimiano, T. Heath, E. Hyvönen, R. Mizoguchi, E. Oren, M. Sabou, E. Simperl. Lecture Notes in Computer Science, vol. 5554 (Springer, Berlin/New York, 2009), pp. 550–564
M. Klusch, A. Leger, D. Martin, M. Paolucci, A. Bernstein, U. Kuster, 3rd international semantic service selection contest – retrieval performance evaluation of matchmakers for semantic web services (S3 contest), in Third International Workshop SMR 2 2009 on Service Matchmaking and Resource Retrieval in the Semantic Web at the 8th International Semantic Web Conference, Busan, 2009
M. Klusch, P. Kapahnke, iSeM: approximated reasoning for adaptive hybrid selection of semantic services, in The Semantic Web: Research and Applications, vol. 6089 (Springer, Berlin, 2010), pp. 30–44
M. Klusch, A. Leger, D. Martin, M. Paolucci, A. Bernstein, U. Küster, 4rd international semantic service selection contest – retrieval performance evaluation of matchmakers for semantic web services (S3 contest), in Fourth International Workshop SMR 2 2009 on Service Matchmaking and Resource Retrieval in the Semantic Web at the 9th International Semantic Web Conference, Busan, 2010
H. Kucera, W.N. Francis, Computational Analysis of Present-Day American English (Brown University Press, Providence, 1967)
U. Lampe, S. Schulte, M. Siebenhaar, D. Schuller, R. Steinmetz, Adaptive matchmaking for RESTful services based on hRESTS and MicroWSMO, in 5th Workshop on Enhanced Web Service Technologies, Ayia Napa, 2010, pp. 10–17
L. Li, I. Horrocks, A software framework for matchmaking based on semantic web technology. Int. J. Electron. Commer. 8(4), 39–60 (2004)
D. Lin, An information-theoretic definition of similarity, in Fifteenth International Conference on Machine Learning, Madison, 1998, pp. 296–304
C. Liu, Y. Peng, J. Chen, Web services escription ontology-based service discovery model, in 2006 IEEE/ WIC/ ACM International Conference on Web Intelligence, Hong Kong, 2006, pp. 633–636
D.C. Manning, P. Raghavan, H. Schütze. Introduction to Information Retrieval (Cambridge University Press, New York, 2008)
S.A. McIlraith, T.C. Son, H. Zeng. Semantic Web Services. IEEE Intell. Syst. 16(2), 46–53 (2001)
G.A. Miller, WordNet: a lexical database for English. Commun. ACM 38(11), 39–41 (1995)
T.M. Mitchell, Machine Learning (McGraw-Hill, New York, 1997)
K.A. Nedas, Munkres’ (Hungarian) Algorithm (2005), Available online at http://konstantinosnedas.com/dev/soft/munkres.htm. Accessed 21 Feb 2011
M. Paolucci, T. Kawamura, T.R. Payne, K.P. Sycara, Importing the semantic web in UDDI, in International Workshop on Web Services, E-Business, and the Semantic Web in Connection with the 14th Conference on Advanced Information Systems Engineering, Toronto, 2002, pp. 225–236
P. Plebani, B. Pernici, URBE: web service retrieval based on similarity evaluation. IEEE Trans. Knowl. Data Eng. 21(11), 1629–1642 (2009)
S.T. Rachev, S. Mittnik, F.J. Fabozzi, M. Focardi, T. Jasic, Financial Econometrics: From Basics to Advanced Modeling Techniques (Wiley, Hoboken, 2007)
R. Rada, H. Mili, E. Bicknell, M. Blettner, Development and application of a metric on semantic nets. IIEEE Trans. Syst. Man Cybern. 19(1), 17–30 (1989)
P. Resnik, Using information content to evaluate semantic similarity in a taxonomy, in Fourteenth International Joint Conference on Artificial Intelligence, Montréal, 1995, pp. 448–453
T. Sakai, N. Kando, On information retrieval metrics designed for evaluation with incomplete relevance assessments. Inf. Retr. 11(5), 447–470 (2008)
S. Schulte, U. Lampe, J. Eckert, R. Steinmetz, LOG4SWS.KOM: self-adapting semantic web service discovery for SAWSDL, in Fourth International Workshop of Software Engineering for Adaptive Service-Oriented Systems, Washington, 2010
T. Syeda-Mahmood, G. Shah, R. Akkiraju, A.-A. Ivan, R. Goodwin, Searching service repositories by combining semantic and ontological matching, in 2005 IEEE International Conference on Web Services, Orlando, 2005, pp. 13–20
I.H. Witten, E. Frank, Data Mining: Practical Machine Learning Tools and Techniques, 2nd edn. (Morgan Kaufmann, San Francisco, 2005)
J. Wooldridge, Introductory Econometrics: A Modern Approach, 4th edn. (South-Western Cengage Learning, Mason, 2008)
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Lampe, U., Schulte, S. (2012). Self-Adaptive Semantic Matchmaking Using COV4SWS.KOM and LOG4SWS.KOM. In: Blake, B., Cabral, L., König-Ries, B., Küster, U., Martin, D. (eds) Semantic Web Services. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28735-0_9
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