KeyOnto: A Hybrid Knowledge Retrieval Model in Law Semantic Web

Abstract

This paper proposes a hybrid knowledge retrieval model KeyOnto, which combines ontology based knowledge retrieval model with traditional Vector Space Model (VSM). KeyOnto model makes use of domain ontology to organize and structure knowledge resources. Documents and queries are represented by concepts and term vectors respectively. Furthermore, ontology based query expansion called K2CM, is introduced to get expanded concepts of a query. Domain specific terms are used to form a term vector for queries and documents. Basing on these vectors, we can evaluate term similarity and concept similarity respectively, and integrate them together. Domain specific thesaurus is used to assist knowledge retrieval. Experiments show that compared with each single model, KeyOnto model improves precision of query result.