Learning Ontologies to Improve the Quality of Automatic Web Service Matching

Automatically finding suitable Web services given a request is a difficult problem because the interface descriptions of Web 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 the Web 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.