  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.
