  Named entities and WordNet words are important in defining the content of a
text in which they occur. Named entities have ontological features, namely,
their aliases, classes, and identifiers. WordNet words also have ontological
features, namely, their synonyms, hypernyms, hyponyms, and senses. Those
features of concepts may be hidden from their textual appearance. Besides,
there are related concepts that do not appear in a query, but can bring out the
meaning of the query if they are added. The traditional constrained spreading
activation algorithms use all relations of a node in the network that will add
unsuitable information into the query. Meanwhile, we only use relations
represented in the query. We propose an ontology-based generalized Vector Space
Model to semantic text search. It discovers relevant latent concepts in a query
by relation constrained spreading activation. Besides, to represent a word
having more than one possible direct sense, it combines the most specific
common hypernym of the remaining undisambiguated multi-senses with the form of
the word. Experiments on a benchmark dataset in terms of the MAP measure for
the retrieval performance show that our model is 41.9% and 29.3% better than
the purely keyword-based model and the traditional constrained spreading
activation model, respectively.

Information Retrieval