Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
IEEE Intelligent Systems
Bringing Semantics to Web Services
IEEE Intelligent Systems
Text Document Categorization by Term Association
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Mining Strong Affinity Association Patterns in Data Sets with Skewed Support Distribution
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
SemRank: ranking complex relationship search results on the semantic web
WWW '05 Proceedings of the 14th international conference on World Wide Web
Ranking Complex Relationships on the Semantic Web
IEEE Internet Computing
Similarity search for web services
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Improving Web Service Discovery by Using Semantic Models
WISE '08 Proceedings of the 9th international conference on Web Information Systems Engineering
A greedy approach with criteria factors for QoS based web service discovery
Proceedings of the 2nd Bangalore Annual Compute Conference
SSERank: semantic search engine for page ranking based on the relations weight
International Journal of Metadata, Semantics and Ontologies
Utilizing the interactive techniques to achieve automated service composition for Web Services
Journal of High Speed Networks
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Semantic Web technology is a promising first step for automated web service discovery. Most current approaches for web service discovery cater to semantic web services, i.e., web services that have associated semantic descriptions. It is unrealistic, however, to expect all new services to have associated semantic descriptions. Furthermore, the descriptions of the vast majority of already existing services do not have explicitly associated semantics. In this paper we present a novel approach for web service discovery that combines semantic and statistical association metrics. Semantic metrics are based on the semantic aspects of relevant ontology. Statistical association metrics are based on the association aspects of web services instances (their inputs and outputs). Specifically, our approach exploits semantic relationship ranking for establishing semantic relevance, and a hyperclique pattern discovery method for grouping web service parameters into meaningful associations. These associations combined by the semantic relevance are then leveraged to discover and rank web services.