Construction of Ontology Based Semantic-Linguistic Feature Vectors for Searching: The Process and Effect

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.