Augmenting web service discovery by cognitive semantics and abduction
Data(s) |
01/09/2009
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Resumo |
We argue that web service discovery technology should help the user navigate a complex problem space by providing suggestions for services which they may not be able to formulate themselves as (s)he lacks the epistemic resources to do so. Free text documents in service environments provide an untapped source of information for augmenting the epistemic state of the user and hence their ability to search effectively for services. A quantitative approach to semantic knowledge representation is adopted in the form of semantic space models computed from these free text documents. Knowledge of the user’s agenda is promoted by associational inferences computed from the semantic space. The inferences are suggestive and aim to promote human abductive reasoning to guide the user from fuzzy search goals into a better understanding of the problem space surrounding the given agenda. Experimental results are discussed based on a complex and realistic planning activity. |
Formato |
application/pdf |
Identificador | |
Publicador |
IEEE |
Relação |
http://eprints.qut.edu.au/29607/1/c29607.pdf DOI:10.1109/WI-IAT.2009.69 Bruza, Peter D., Barros, Alistair P., & Kaiser, Matthias (2009) Augmenting web service discovery by cognitive semantics and abduction. In Proceedings of Web Intelligence and Intelligent Agent Technologies, 2009. WI-IAT '09. IEEE/WIC/ACM International Joint Conferences, IEEE, University of Milano-Bicocca, Milan, pp. 403-410. |
Direitos |
Copyright 2009 IEEE Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. |
Fonte |
Faculty of Science and Technology; Institute for Creative Industries and Innovation; School of Information Systems; Science & Engineering Faculty |
Palavras-Chave | #080109 Pattern Recognition and Data Mining #080107 Natural Language Processing #080704 Information Retrieval and Web Search |
Tipo |
Conference Paper |