A brief survey of web data extraction tools
ACM SIGMOD Record
Extracting ontological concepts for tendering conceptual structures
Data & Knowledge Engineering
Swoogle: a search and metadata engine for the semantic web
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Object-level ranking: bringing order to Web objects
WWW '05 Proceedings of the 14th international conference on World Wide Web
Supporting application development in the semantic web
ACM Transactions on Internet Technology (TOIT)
Possible Ontologies: How Reality Constrains the Development of Relevant Ontologies
IEEE Internet Computing
Building ontological relationships: A new approach
Journal of the American Society for Information Science and Technology
Finding and ranking knowledge on the semantic web
ISWC'05 Proceedings of the 4th international conference on The Semantic Web
HSWS: enhancing efficiency of web search engine via semantic web
Proceedings of the International Conference on Management of Emergent Digital EcoSystems
Semantic Clustering of Web Documents: An Ontology based Approach Using Swarm Intelligence
International Journal of Information Technology and Web Engineering
Adapting domain ontology for personalized knowledge search and recommendation
Information and Management
A hybrid approach using pso and K-means for semantic clustering of web documents
Journal of Web Engineering
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Many web search engines retrieve enormous amounts of irrelevant information in answer to users' queries. The semantic web provides a promising approach to improve search operation. For specific domains, ontologies can capture concepts to help machines deal with data semantically. Our aim in writing this paper was to show how to measure the closeness (relevancy) of retrieved web sites to user query-concepts and re-rank them accordingly. We therefore proposed a new relevancy measure to re-rank retrieved documents. We termed the approach ''ontology concepts'' and it on the domain of electronic commerce. Results suggested that we could re-rank the retrieved documents (web sites) according to their relevancy to the search query. Our method depends on the frequency of the ''ontology concepts'' in the retrieved documents and uses this to compute their relevancy.