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Purely keyword-based text search is not satisfactory because named entitiesand WordNet words are also important elements to define the content of adocument or a query in which they occur. Named entities have ontologicalfeatures, namely, their aliases, classes, and identifiers. Words in WordNetalso have ontological features, namely, their synonyms, hypernyms, hyponyms,and senses. Those features of concepts may be hidden from their textualappearance. 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. We propose anontology-based generalized Vector Space Model to semantic text search. Itexploits ontological features of named entities and WordNet words, and developsa query-oriented spreading activation algorithm to expand queries. In addition,it combines and utilizes advantages of different ontologies for semanticannotation and searching. Experiments on a benchmark dataset show that, interms of the MAP measure, our model is 42.5% better than the purelykeyword-based model, and 32.3% and 15.9% respectively better than the onesusing only WordNet or named entities.  Keywords: semantic search, spreading activation, ontology, named entity,WordNet.

Combining Named Entities with WordNet and Using Query-Oriented Spreading Activation for Semantic Text Search