On Type-Aware Entity Retrieval

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.Comment: Proceedings of the 3rd ACM International Conference on the Theory of Information Retrieval (ICTIR '17), 201

Unsupervised Learning of an Extensive and Usable Taxonomy for DBpedia

ABSTRACT In the digital era, Wikipedia represents a comprehensive cross-domain source of knowledge with millions of contributors. The DBpedia project transforms Wikipedia content into RDF and currently plays a crucial role in the Web of Data as a central multilingual interlinking hub. However, its main classification system depends on human curation, which causes it to lack coverage, resulting in a large amount of untyped resources. We present an unsupervised approach that automatically learns a taxonomy from the Wikipedia category system and extensively assigns types to DBpedia entities, through the combination of several interdisciplinary techniques. It provides a robust backbone for DBpedia knowledge and has the benefit of being easy to understand for end users. Crowdsourced online evaluations demonstrate that our strategy outperforms state-of-the-art approaches both in terms of coverage and intuitiveness