A vector space model for automatic indexing
Communications of the ACM
OntoSeek: Content-Based Access to the Web
IEEE Intelligent Systems
Ontobroker: Ontology Based Access to Distributed and Semi-Structured Information
DS-8 Proceedings of the IFIP TC2/WG2.6 Eighth Working Conference on Database Semantics- Semantic Issues in Multimedia Systems
Using information content to evaluate semantic similarity in a taxonomy
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
An efficient ontology-based expert peering system
GbRPR'07 Proceedings of the 6th IAPR-TC-15 international conference on Graph-based representations in pattern recognition
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In this paper, we present a novel method to find the right expert who matches a certain project well. The idea behind this method includes building domain ontologies to describe projects and experts and calculating similarities between projects and domain experts for matching. The developed system consists of four main components: ontology building, document formalization, similarity calculation and user interface. First, we utilize Protégé to develop the predetermined domain ontologies in which some related concepts are defined. Then, documents concerning experts and projects are formalized by means of concept trees with weights. This process can be done either automatically or manually. Finally, a new method that integrates node-based and edge-based approach is proposed to measure the semantic similarities between projects and experts with the help of the domain ontologies. The experimental results show that the developed information matching system can reach the satisfied recall and precision.