  Traditional information retrieval systems represent documents and queries by
keyword sets. However, the content of a document or a query is mainly defined
by both keywords and named entities occurring in it. Named entities have
ontological features, namely, their aliases, classes, and identifiers, which
are hidden from their textual appearance. Besides, the meaning of a query may
imply latent named entities that are related to the apparent ones in the query.
We propose an ontology-based generalized vector space model to semantic text
search. It exploits ontological features of named entities and their latently
related ones to reveal the semantics of documents and queries. We also propose
a framework to combine different ontologies to take their complementary
advantages for semantic annotation and searching. Experiments on a benchmark
dataset show better search quality of our model to other ones.
