On Type-Aware Entity Retrieval

TL;DR

This paper investigates how hierarchical entity type information can enhance entity retrieval, analyzing the impact of taxonomy choice, representation, and integration with term-based methods in an idealized setting.

Contribution

It provides a comprehensive analysis of utilizing type hierarchies in entity retrieval, highlighting the importance of large, specific taxonomies and the combination with term similarity.

Findings

Type information improves retrieval with large, specific taxonomies

Hierarchical representation impacts retrieval effectiveness

Combining type and term similarity yields better results

Abstract

Today, the practice of returning entities from a knowledge base in response to search queries has become widespread. One of the distinctive characteristics of entities is that they are typed, i.e., assigned to some hierarchically organized type system (type taxonomy). The primary objective of this paper is to gain a better understanding of how entity type information can be utilized in entity retrieval. We perform this investigation in an idealized "oracle" setting, assuming that we know the distribution of target types of the relevant entities for a given query. We perform a thorough analysis of three main aspects: (i) the choice of type taxonomy, (ii) the representation of hierarchical type information, and (iii) the combination of type-based and term-based similarity in the retrieval model. Using a standard entity search test collection based on DBpedia, we find that type information proves most useful when using large type taxonomies that provide very specific types. We provide further insights on the extensional coverage of entities and on the utility of target types.