Toward SWSs Discovery: Mapping from WSDL to OWL-S Based on Ontology Search and Standardization Engine
Issue No. 05 - May (2013 vol. 25)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TKDE.2012.25
Tamer Ahmed Farrag , Mansoura University, Mansoura
Ahmed Ibrahim Saleh , Mansoura University, Mansoura
Hesham Arafat Ali , Mansoura University, Mansoura
Semantic Web Services (SWSs) represent the most recent and revolutionary technology developed for machine-to-machine interaction on the web 3.0. As for the conventional web services, the problem of discovering and selecting the most suitable web service represents a challenge for SWSs to be widely used. In this paper, we propose a mapping algorithm that facilitates the redefinition of the conventional web services annotations (i.e., WSDL) using semantic annotations (i.e., OWL-S). This algorithm will be a part of a new discovery mechanism that relies on the semantic annotations of the web services to perform its task. The “local ontology repository” and “ontology search and standardization engine” are the backbone of this algorithm. Both of them target to define any data type in the system using a standard ontology-based concept. The originality of the proposed mapping algorithm is its applicability and consideration of the standardization problem. The proposed algorithm is implemented and its components are validated using some test collections and real examples. An experimental test of the proposed techniques is reported, showing the impact of the proposed algorithm in decreasing the time and the effort of the mapping process. Moreover, the experimental results promises that the proposed algorithm will have a positive impact on the discovery process as a whole.
Ontologies, Web services, Standardization, Semantics, OWL, Databases, ontology-based standardization, Semantic Web Service, ontology, mapping, WSDL, OWL-S
A. I. Saleh, T. A. Farrag and H. A. Ali, "Toward SWSs Discovery: Mapping from WSDL to OWL-S Based on Ontology Search and Standardization Engine," in IEEE Transactions on Knowledge & Data Engineering, vol. 25, no. , pp. 1135-1147, 2013.