A Concise Conversion Model for Improving the RDF Expression of ConceptNet Knowledge Base

Hua Chen, Antoine Trouve, Kazuaki J. Murakami, Akira Fukuda

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

抄録

With the explosive growth of information on the Web, Semantic Web and related technologies such as linked data and commonsense knowledge bases, have been introduced. ConceptNet is a commonsense knowledge base, which is available for public use in CSV and JSON format; it provides a semantic graph that describes general human knowledge and how it is expressed in natural language. Recently, an RDF presentation of ConceptNet called ConceptRDF has been proposed for better use in different fields; however, it has some problems (e.g., information of concepts is sometimes misexpressed) caused by the improper conversion model. In this paper, we propose a concise conversion model to improve the RDF expression of ConceptNet. We convert the ConceptNet into RDF format and perform some experiments with the conversion results. The experimental results show that our conversion model can fully express the information of ConceptNet, which is suitable for developing many intelligent applications.

本文言語英語
ホスト出版物のタイトルArtificial Intelligence and Robotics
編集者Xing Xu, Huimin Lu
出版社Springer Verlag
ページ213-221
ページ数9
ISBN(印刷版)9783319698762
DOI
出版ステータス出版済み - 1 1 2018
イベント2nd International Symposium on Artificial Intelligence and Robotics, ISAIR 2017 - Kitakyushu, 日本
継続期間: 11 25 201711 26 2017

出版物シリーズ

名前Studies in Computational Intelligence
752
ISSN(印刷版)1860-949X

その他

その他2nd International Symposium on Artificial Intelligence and Robotics, ISAIR 2017
Country日本
CityKitakyushu
Period11/25/1711/26/17

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence

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