Abstract
In this paper, we elaborate on an approach to construction of semantic-linguistic feature vectors (FV) that are used in search. These FVs are built based on domain semantics encoded in an ontology and enhanced by a relevant terminology from Web documents. The value of this approach is twofold. First, it captures relevant semantics from an ontology, and second, it accounts for statistically significant collocations of other terms and phrases in relation to the ontology entities. The contribution of this paper is the FV construction process and its evaluation. Recommendations and lessons learnt are laid down. © 2009 Springer-Verlag Berlin Heidelberg.
Cite
CITATION STYLE
Tomassen, S. L., & Strasunskas, D. (2009). Semantic-linguistic feature vectors for search: Unsupervised construction and experimental validation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5926 LNCS, pp. 199–215). https://doi.org/10.1007/978-3-642-10871-6_14
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