A Comparison of Word Embeddings and N-gram Models for DBpedia Type and Invalid Entity Detection

Autor:	Hanqing Zhou, Amal Zouaq, Diana Inkpen
Jazyk:	angličtina
Rok vydání:	2018
Předmět:	semantic web DBpedia entity embedding n-grams type identification entity identification data mining machine learning Information technology T58.5-58.64
Zdroj:	Information, Vol 10, Iss 1, p 6 (2018)
Druh dokumentu:	article
ISSN:	2078-2489
DOI:	10.3390/info10010006
Popis:	This article presents and evaluates a method for the detection of DBpedia types and entities that can be used for knowledge base completion and maintenance. This method compares entity embeddings with traditional N-gram models coupled with clustering and classification. We tackle two challenges: (a) the detection of entity types, which can be used to detect invalid DBpedia types and assign DBpedia types for type-less entities; and (b) the detection of invalid entities in the resource description of a DBpedia entity. Our results show that entity embeddings outperform n-gram models for type and entity detection and can contribute to the improvement of DBpedia’s quality, maintenance, and evolution.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/69900d043e6042e0974c5f83dc58d52e Zobrazit plný text záznamu View record in DOAJ Plný text ve formátu PDF Plný text ve formátu HTML
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