Using Ontologies for Domain Information Retrieval
DEXA '00 Proceedings of the 11th International Workshop on Database and Expert Systems Applications
An Adaptation of the Vector-Space Model for Ontology-Based Information Retrieval
IEEE Transactions on Knowledge and Data Engineering
The Google Similarity Distance
IEEE Transactions on Knowledge and Data Engineering
A Categorization Scheme for Semantic Web Search Engines
AICCSA '06 Proceedings of the IEEE International Conference on Computer Systems and Applications
A survey and classification of semantic search approaches
International Journal of Metadata, Semantics and Ontologies
Toward a New Generation of Semantic Web Applications
IEEE Intelligent Systems
ECIR'05 Proceedings of the 27th European conference on Advances in Information Retrieval Research
ASWC '09 Proceedings of the 4th Asian Conference on The Semantic Web
Measuring intrinsic quality of semantic search based on feature vectors
International Journal of Metadata, Semantics and Ontologies
Proceedings of the 11th International Conference on Information Integration and Web-based Applications & Services
Proceedings of the 11th International Conference on Information Integration and Web-based Applications & Services
International Journal of Web and Grid Services
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Search is among the most frequent activities on the Web. However, the search activity still requires extra efforts in order to get satisfactory results. One of the reasons is heterogeneous information resources and exponential growth of information. The problem of heterogeneity arises as a result of discipline specific language used even in domain specific documents. This particular problem we tackle in this paper. We propose an approach to construct semantic-linguistic feature vectors (FV). The FVs are built based on domain semantics encoded in an ontology and enhanced by a relevant terminology from documents on the Web. Semantic information from the ontologies is also used to expand the user queries and the FVs are used to filter and rank the retrieved documents. The strength of this approach is twofold. First, it is grounded on relevant semantics from an ontology, and second, it accounts for statistically significant collocations of other terms and phrases in relation to the ontology entities. In this paper, we explain how these FVs are constructed and what effect they have on search performance.