Ontology-Based Query Expansion with Latently Related Named Entities for Semantic Text Search

TL;DR

This paper introduces an ontology-based vector space model for semantic text search that leverages ontological features and latent related named entities to improve retrieval accuracy.

Contribution

It presents a novel generalized vector space model utilizing ontological features and a framework for combining multiple ontologies for enhanced semantic search.

Findings

Improved search quality over existing models

Effective use of ontological features for semantic annotation

Successful integration of multiple ontologies

Abstract

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.