Taxonomical hierarchy of canonicalized relations from multiple Knowledge Bases

Abstract

This work addresses two important questions pertinent to Relation Extraction (RE). First, what are all possible relations that could exist between any two given entity types? Second, how do we define an unambiguous taxonomical (is-a) hierarchy among the identified relations? To address the first question, we use three resources Wikipedia Infobox, Wikidata, and DBpedia. This study focuses on relations between person, organization and location entity types. We exploit Wikidata and DBpedia in a data-driven manner, and Wikipedia Infobox templates manually to generate lists of relations. Further, to address the second question, we canonicalize, filter, and combine the identified relations from the three resources to construct a taxonomical hierarchy. This hierarchy contains 623 canonical relations with the highest contribution from Wikipedia Infobox followed by DBpedia and Wikidata. The generated relation list subsumes an average of 85% of relations from RE datasets when entity types are restricted 1 .

Budget Sensitive Reannotation of Noisy Relation Classification Data Using Label Hierarchy

“…al. [23], have collected more than 600 relations between Person, Organization, and Location entity types from multiple knowledge bases and arranged them in a taxonomical hierarchy following is-a relationship. They manually created the relation hierarchy but did not show any applications of the generated hierarchy.…”

“…Our reannotation strategy is based on the taxonomic-hierarchy prepared by Parekh et al [23]. Following their work, we arranged all relations from the TA-CRED dataset in a taxonomic hierarchy.…”