Towards Entity Spaces

Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020), pages 2129–2137 Marseille, 11–16 May 2020 c⃝ European Language Resources Association (ELRA), licensed under CC-BY-NC 2129 Towards Entity Spaces Marieke van Erp∗, Paul Groth† ∗KNAW Humanities Cluster - DHLab, Amsterdam, NL †University of Amsterdam, Amsterdam, NL marieke.van.erp@dh.huc.knaw.nl, p.groth@uva.nl Abstract Entities are a central element of knowledge bases and are important input to many knowledge-centric tasks including text analysis. For example, they allow us to find documents relevant to a specific entity irrespective of the underlying syntactic expression within a document. However, the entities that are commonly represented in knowledge bases are often a simplification of what is truly being referred to in text. For example, in a knowledge base, we may have an entity for Germany as a country but not for the more fuzzy concept of Germany that covers notions of German Population, German Drivers, and the German Government. Inspired by recent advances in contextual word embeddings, we introduce the concept of entity spaces - specific representations of a set of associated entities with near-identity. Thus, these entity spaces provide a handle to an amorphous grouping of entities. We developed a proof-of-concept for English showing how, through the introduction of entity spaces in the form of disambiguation pages, the recall of entity linking can be improved. Keywords: entity, identity, knowledge representation, entity linking 1. Introduction Entities are a central element for knowledge bases and text analysis tasks (Balog, 2018). However, the way in which entities are represented in knowledge bases and how subsequent tools use these representations are a sim- plification of the complexity of many entities. For ex- ample, the entity Germany in Wikidata as represented by wikidata:Q183 focuses on its properties as a location and geopolitical entity due to its membership as an in- stance of sovereign state, country, federal state, repub- lic, social state, legal state, and administrative territorial entity. Similarly, in DBpedia (version 2016-10), Ger- many is represented as entity of type populated place and some subtypes such as yago:WikicatFederalCountries and yago:WikicatMemberStatesOfTheEuropeanUnion.1 However, when the term Germany is used in text, it can take on many meanings that all have ‘something to do’ with Germany as it is represented in knowledge bases, but are all not quite the same: (1) Germany imported 47,600 sheep from Britain last year, nearly half of total imports. (2) German July car registrations up 14.2 pct yr / yr. (3) Australia last won the Davis Cup in 1986, but they were beaten finalists against Germany three years ago under Fraser’s guidance. In Example (1), Germany refers partly to the location, but a location usually cannot take on an active role, such that the entity ‘importing’ the sheep is most likely a referent to the German meat industry. Germany in Example (2), refers to the German population buying and registering more cars than a year before. Finally, in Example (3), Germany refers to the German Davis Cup team from 1993 (the news article is from 1996). In the AIDA-YAGO …

Content truncated to 500 words