Semantic Search by Latent Ontological Features

Abstract

Both named entities and keywords are important in defining the content of a text in which they occur. In particular, people often use named entities in information search. However, named entities have ontological features, namely, their aliases, classes, and identifiers, which are hidden from their textual appearance. We propose ontology-based extensions of the traditional Vector Space Model that explore different combinations of those latent ontological features with keywords for text retrieval. Our experiments on benchmark datasets show better search quality of the proposed models as compared to the purely keyword-based model, and their advantages for both text retrieval and representation of documents and queries.The usefulness and explosion of information on the WWW have been challenging research on information retrieval, regarding how that rich and huge resource of information should be exploited efficiently. Information retrieval is not a new area but still attracts much research effort, social and industrial interests, because, on the one hand, it is important for searching required information and, on the other hand, there are still many open problems to be solved to enhance search performance. Retrieval precision and recall could be improved by developing appropriate models, typically as similarity-based, 9, 23) probabilistic relevance, 27) or probabilistic inference 28) ones. Semantic annotation, representation, and processing of documents and queries are another way to obtain better search quality. 6,10,12,29)

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