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Cost Minimization and Social Fairness for Spatial Crowdsourcing Tasks Read chapter Read first chapter

2016 | OriginalPaper | Chapter

Cross-Lingual Type Inference

Authors : Bo Xu, Yi Zhang, Jiaqing Liang, Yanghua Xiao, Seung-won Hwang, Wei Wang

Published in: Database Systems for Advanced Applications

Publisher: Springer International Publishing

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Abstract

Entity typing is an essential task for constructing a knowledge base. However, many non-English knowledge bases fail to type their entities due to the absence of a reasonable local hierarchical taxonomy. Since constructing a widely accepted taxonomy is a hard problem, we propose to type these non-English entities with some widely accepted taxonomies in English, such as DBpedia, Yago and Freebase. We define this problem as cross-lingual type inference. In this paper, we present CUTE to type Chinese entities with DBpedia types. First we exploit the cross-lingual entity linking between Chinese and English entities to construct the training data. Then we propose a multi-label hierarchical classification algorithm to type these Chinese entities. Experimental results show the effectiveness and efficiency of our method.

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previous chapter Knowledge Graph Completion via Local Semantic Contexts
next chapter Benchmarking Semantic Capabilities of Analogy Querying Algorithms
Footnotes
1
In this paper, we strictly differentiate “category” from “type”. “Category” always refer to the part of the knowledge base such as Baidu Baike and Wikipedia named as “category”. Most of these categories actually are only tags of an entity. Instead “type” always refer to the class that an entity can be classified into.
 
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Metadata
Title
Cross-Lingual Type Inference
Authors
Bo Xu
Yi Zhang
Jiaqing Liang
Yanghua Xiao
Seung-won Hwang
Wei Wang
Copyright Year
2016
Book
Database Systems for Advanced Applications

Print ISBN: 978-3-319-32024-3

Electronic ISBN: 978-3-319-32025-0

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-32025-0_28

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