Word sense disambiguation for free-text indexing using a massive semantic network
CIKM '93 Proceedings of the second international conference on Information and knowledge management
Text retrieval using inference in semantic metanetworks
Text retrieval using inference in semantic metanetworks
Ontologies: a silver bullet for knowledge management and electronic commerce
Ontologies: a silver bullet for knowledge management and electronic commerce
IEEE Intelligent Systems
Semantic Matching of Web Services Capabilities
ISWC '02 Proceedings of the First International Semantic Web Conference on The Semantic Web
DAMLJessKB: A Tool for Reasoning with the Semantic Web
IEEE Intelligent Systems
Ontology support for web service processes
Proceedings of the 9th European software engineering conference held jointly with 11th ACM SIGSOFT international symposium on Foundations of software engineering
Learning to match ontologies on the Semantic Web
The VLDB Journal — The International Journal on Very Large Data Bases
Flexible Interface Matching for Web-Service Discovery
WISE '03 Proceedings of the Fourth International Conference on Web Information Systems Engineering
Improved semantic retrieval method based on domain ontology
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 1
Quantified matchmaking of heterogeneous services
WISE'06 Proceedings of the 7th international conference on Web Information Systems
Semantic web services matchmaking: Semantic distance-based approach
Computers and Electrical Engineering
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A key issue in web services is matching that involves comparing user requests with advertised services and finding the best available ones. In semantic web services, an ontology is used by the matching system to determine the semantic relationship between the requests and the registered services. In this paper, we propose that the semantic relationship can be measured quantitatively in order to provide a more precise similarity measures between the requested and advertised services and to produce a better ranking of relevant services. We proposes and develops a Semantic Distance Measure that is tailored to provide a quantitative measure that indicates similarity between advertised and requested services. We establish that such a measure is an effective means of discriminating services at a level of granularity that is able to enhance the matching process in semantic web services.