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Information 2019, 10(1), 6; https://doi.org/10.3390/info10010006

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

School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa ON K1N 6N5, Canada
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This paper is an extended version of our conference paper: Hanqing Zhou, Amal Zouaq, and Diana Inkpen. DBpedia Entity Type Detection using Entity Embeddings and N-Gram Models. In Proceedings of the International Conference on Knowledge Engineering and Semantic Web (KESW 2017), Szczecin, Poland, 8–10 November 2017, pp. 309–322.
Received: 6 November 2018 / Revised: 14 December 2018 / Accepted: 20 December 2018 / Published: 25 December 2018
(This article belongs to the Special Issue Knowledge Engineering and Semantic Web)
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Abstract

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. View Full-Text
Keywords: semantic web; DBpedia; entity embedding; n-grams; type identification; entity identification; data mining; machine learning semantic web; DBpedia; entity embedding; n-grams; type identification; entity identification; data mining; machine learning
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Zhou, H.; Zouaq, A.; Inkpen, D. A Comparison of Word Embeddings and N-gram Models for DBpedia Type and Invalid Entity Detection. Information 2019, 10, 6.

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