Index-Based Semantic Tagging for Efficient Query Interpretation

Abstract

Modern search engines are evolving beyond ad hoc document retrieval. Nowadays, the information needs of the users can be directly satisfied through entity-oriented search, by ranking the entities or attributes that better relate to the query, as opposed to the documents that contain the best matching terms. One of the challenges in entity-oriented search is efficient query interpretation. In particular, the task of semantic tagging, for the identification of entity types in query parts, is central to understanding user intent. We compare two approaches for semantic tagging, within a single domain, one based on a Sesame triple store and another one based on a Lucene index. This provides a segmentation and annotation of the query based on the most probable entity types, leading to query classification and its subsequent interpretation. We evaluate the run time performance for the two strategies and find that there is a statistically significant speedup, of at least four times, for the index-based strategy over the triple store strategy.