Exploring Combinations of Ontological Features and Keywords for Text Retrieval
This work addresses information retrieval for users needing more accurate text search, but it is incremental as it builds on existing models.
The paper tackled the problem of enhancing information retrieval by combining ontological features with keywords, showing that the proposed models outperform keyword-based Lucene in performance.
Named entities have been considered and combined with keywords to enhance information retrieval performance. However, there is not yet a formal and complete model that takes into account entity names, classes, and identifiers together. Our work explores various adaptations of the traditional Vector Space Model that combine different ontological features with keywords, and in different ways. It shows better performance of the proposed models as compared to the keyword-based Lucene, and their advantages for both text retrieval and representation of documents and queries.