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doi:10.1002/asi.v61:5

Cited by 60 publications

(3 citation statements)

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“…The underlying information need may be "camera brands" or "multinational corporations headquartered in Japan" or something else. Domain-specific applications of example-based search include, for instance, finding people with expertise similar to that of others within an organization [4,28]. A more generic application area is concept expansion for knowledge base population (cf.…”

Section: Similar Entity Searchmentioning

confidence: 99%

“…Instead of using individual terms (unigrams), the vector representation may also be made up of n-grams or keyphrases [27,28]. Specifically, Hoffart et al [27] introduce the keyphrase overlap relatedness (KORE) measure, and present approximation techniques, based on min-hash sketches and locality-sensitive hashing, for efficient computation.…”

Section: Term-based Similaritymentioning

confidence: 99%

“…shown that contextual factors (e.g., availability, experience, physical proximity) also play a role [28].…”

Section: Semantically Enriched Models For Entity Rankingmentioning

confidence: 99%

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Semantically Enriched Models for Entity Ranking

Balog

2018

Entity-Oriented Search

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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 retrieval 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.

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Section: Similar Entity Searchmentioning

confidence: 99%