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

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

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)...

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Bibliographic Details
Main Authors: Hanqing Zhou, Amal Zouaq, Diana Inkpen
Format: Article
Language:English
Published: MDPI AG 2018-12-01
Series:Information
Subjects:
semantic web
DBpedia
entity embedding
n-grams
type identification
entity identification
data mining
machine learning
Online Access:http://www.mdpi.com/2078-2489/10/1/6

Internet

http://www.mdpi.com/2078-2489/10/1/6

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