Abstract
In this paper we introduce a novel unsupervised ontology learning approach, which can be used to automatically derive a reference ontology from a corpus of web services for annotating semantically the Web services in the absence of a core ontology. Our approach relies on shallow parsing technique from natural language processing in order to identify grammatical patterns of web service message element/part names and exploit them in construction of the ontology. The generated ontology is further enriched by introducing relationships between similar concepts. The experimental results on a set of global Web services indicate that the proposed ontology learning approach generates an ontology, which can be used to automatically annotate around 52% of element part and field names in a large corpus of heterogeneous Web services.
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References
Guo, H., Ivan, A., Akkiraju, R., Goodwin, R.: Learning Ontologies to Improve the Quality of Automatic Web Service Matching. In: IEEE Int. Conference on Web Services (ICWS 2007), pp. 118–125 (2007)
Küngas, P., Dumas, M.: Cost-Effective Semantic Annotation of XML Schemas and Web Service Interfaces. In: IEEE Int. Conference on Services Computing, pp. 372–379. IEEE Computer Society, Los Alamitos (2009)
European Collaborative Clamour Project: Linguistic Work Package Report (2000), http://www.statistics.gov.uk/methods_quality/clamour/coordination/downloads/Clamourdec2000IRn2.doc
Lieber, R.: Morphology and Lexical Semantics. Cambridge University Press, Cambridge (2004)
Bourigault, D., Jacquemin, C.: Term extraction + term clustering: an integrated platform for computer-aided terminology. In: 9th Conference on European Chapter of the Association for Computational Linguistics, pp. 15–22. ACL, Morristown (1999)
Sabou, M., Wroe, C., Goble, C., Mishne, G.: Learning domain ontologies for Web service descriptions: An experiment in bioinformatics. In: Proceedings of the 14th International Conference on World Wide Web, pp. 190–198. ACM, Japan (2005)
Heß, A., Johnston, E., Kushmerick, N.: ASSAM: A Tool for Semi-Automatically Annotating Semantic Web Services. In: McIlraith, S.A., Plexousakis, D., van Harmelen, F. (eds.) ISWC 2004. LNCS, vol. 3298, pp. 320–334. Springer, Heidelberg (2004)
Pirrò, G., Seco, N.: Design, Implementation and Evaluation of a New Similarity Metric Combining Feature and Intrinsic Information Content. In: Meersman, R., Tari, Z. (eds.) OTM 2008, Part II. LNCS, vol. 5332, pp. 1271–1288. Springer, Heidelberg (2008)
Lin, D.: Dependency-based Evaluation of MINIPAR. In: Workshop on the Evaluation of Parsing Systems, First Int. Conf. on Learning Resources and Evaluation, Spain (1998)
Miller, G.A.: WordNet: A Lexical Database for English. Communications of the ACM 38(11), 39–41 (1995)
Hu, W., Qu, Y.: Falcon-AO: A practical ontology matching system. Web Semantic 6(3), 237–239 (2008)
Zhou, L.: Ontology learning: State of the art and open issues. Information Technology and Management 8, 241–252 (2007)
Gruber, T.R.: Toward principles for the design of ontologies used for knowledge sharing. Journal of Human Computer Studies 43(5), 907–928 (1993)
Radiant: WSDL-S/SAWSDL Annotation Tool, http://lsdis.cs.uga.edu/projects/meteor-s/downloads/index.php?page=1
Kopecký, J., Vitvar, T., Bournez, C., Farrell, J.: SAWSDL: Semantic Annotations for WSDL and XML Schema. IEEE Internet Computing 11(6), 60–67 (2007)
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Mokarizadeh, S., Küngas, P., Matskin, M. (2010). Ontology Learning for Cost-Effective Large-Scale Semantic Annotation of Web Service Interfaces. In: Cimiano, P., Pinto, H.S. (eds) Knowledge Engineering and Management by the Masses. EKAW 2010. Lecture Notes in Computer Science(), vol 6317. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16438-5_30
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DOI: https://doi.org/10.1007/978-3-642-16438-5_30
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