Semantic Search by Latent Ontological Features
This work addresses the need for more effective semantic search tools for users relying on named entities, though it is incremental as it extends existing models.
The paper tackled the problem of improving text retrieval by incorporating latent ontological features of named entities, such as aliases and classes, into the Vector Space Model, resulting in better search quality on benchmark datasets compared to keyword-only models.
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.