Ontology-Based Query Expansion with Latently Related Named Entities for Semantic Text Search
This work addresses the challenge of enhancing information retrieval for users by better capturing document and query semantics, though it is incremental as it builds on existing ontology-based methods.
The paper tackled the problem of semantic text search by incorporating ontological features of named entities and their latent relations into a generalized vector space model, resulting in improved search quality on a benchmark dataset.
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.