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Published Articles >> Table of Contents >> Abstract
IEEE International Conference on Web Services (ICWS 2007)
pp. 118-125
Learning Ontologies to Improve the Quality of Automatic Web Service Matching
Hui Guo, Stony Brook University
Anca Ivan, IBM TJ Watson Research Center
Rama Akkiraju, IBM TJ Watson Research Center
Richard Goodwin, IBM TJ Watson Research Center
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DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICWS.2007.114
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| Abstract |
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Automatically finding suitable Web services given a request
is a difficult problem because the interface descriptions
ofWeb services are often terse and cryptic. Dictionary
and information retrieval based techniques have proven
useful in disambiguating the semantics of service descriptions,
but they are limited in their capability to consider
the relationships between the words describing theWeb services.
Current ontology-based approaches typically require
a user to explicitly create domain ontologies. This paper
presents a novel technique that significantly improves the
quality of semantic Web service matching by (1) automatically
generating ontologies based on Web service descriptions
and (2) using these ontologies to guide the mapping
between Web services. Our approach differs from earlier
work on service matching by considering the relationship
between words rather than treating them as a bag of unrelated
words. The experimental results indicate that with
our unsupervised approach we can eliminate up to 70% of
incorrect matches that are made by dictionary-based approaches.
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 Additional Information
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Citation:
Hui Guo, Anca Ivan, Rama Akkiraju, Richard Goodwin,
"Learning Ontologies to Improve the Quality of Automatic Web Service Matching,"
icws,
pp. 118-125,
IEEE International Conference on Web Services (ICWS 2007),
2007
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