A Generalized Vector Space Model for Ontology-Based Information Retrieval
This work addresses information retrieval for users needing more accurate search results by leveraging ontological data, but it appears incremental as it builds on existing vector space models.
The authors tackled the problem of improving information retrieval by integrating named entities and keywords into a generalized vector space model, incorporating ontological features like aliases, classes, and identifiers, and tested it on a TREC dataset.
Named entities (NE) are objects that are referred to by names such as people, organizations and locations. Named entities and keywords are important to the meaning of a document. We propose a generalized vector space model that combines named entities and keywords. In the model, we take into account different ontological features of named entities, namely, aliases, classes and identifiers. Moreover, we use entity classes to represent the latent information of interrogative words in Wh-queries, which are ignored in traditional keyword-based searching. We have implemented and tested the proposed model on a TREC dataset, as presented and discussed in the paper.