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Cluster Computing
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05-04-2017

Knowledge entity learning and representation for ontology matching based on deep neural networks

Authors: Lirong Qiu, Jia Yu, Qiumei Pu, Chuncheng Xiang

Published in: Cluster Computing | Issue 2/2017

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Abstract

We study the task of ontology matching that is used mainly for solving the semantic heterogeneity problems, which concentrates on finding semantically related entities between different ontologies. Many previous works exploit the character-level or token-level information of the descriptions of an entity in ontology directly when applying the string-based matcher or token based matcher to find the corresponding entities. They ignored the higher level correlations between different descriptions of an entity. To address this problem, we propose a representation learning method based on deep neural networks which aim at learning the high level abstract representations of the input entity. Particularly, the representations of the entities are learned in an unsupervised way firstly, and then fine-tuned in a supervised manner with the training data. The experiment results show that our approaches can learn useful representations for entities from its descriptive information to better measure the similarity between entities.
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Literature
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Metadata
Title
Knowledge entity learning and representation for ontology matching based on deep neural networks
Authors
Lirong Qiu
Jia Yu
Qiumei Pu
Chuncheng Xiang
Publication date
05-04-2017
Publisher
Springer US
Published in
Cluster Computing / Issue 2/2017
Print ISSN: 1386-7857
Electronic ISSN: 1573-7543
DOI
https://doi.org/10.1007/s10586-017-0844-1

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