Semantically Enriched Models for Entity Ranking [chapter]

Krisztian Balog
2018 Advanced Topics in Information Retrieval  
Most of our efforts in the previous chapter have revolved around constructing termbased representations of entities. These representations can then be ranked using direct adaptations of existing document retrieval models. On the one hand, the resulting approaches are robust and effective across a broad range of application scenarios. On the other hand, these term-based models have little awareness of what it takes to be an entity. Perhaps the most exciting challenge and opportunity in entity
more » ... rieval is how to leverage entity-specific properties-attributes, types, and relationships-to improve retrieval performance. This requires a departure from purely term-based approaches toward more semantically informed representations. This change of direction is supported by the emergence of knowledge bases over the past decade (cf. Sect. 2.3). Knowledge bases organize information about entities in a structured and semantically meaningful way. For us, semantics is taken to be synonymous with structure (more precisely, with references to meaningful structure). Our efforts in this chapter are driven by the following question: How can one leverage structured knowledge repositories in entity retrieval? At its core, the entity ranking task (and most IR tasks for that matter) boils down to the problem of matching representations. That is, computing similarities between representations of queries (information needs) and those of entities (information objects). The question then becomes: How to preserve and represent structure associated with entities? Importantly, to be able to make use of richer (i.e., semantic) entity representations during matching, queries also need to have correspondingly enriched representations. For example, if types of entities are represented as semantic units, as opposed to sequences of words, then we also need to know the target types of the query. For now, we shall assume that we are provided with such enriched queries, which will be referred to as keyword++ queries.
doi:10.1007/978-3-319-93935-3_4 fatcat:xsri53xoxzc5rmgk4fp6kqj7ti