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

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

doi:10.1007/978-3-319-69877-9_23

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

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

Advanced Software Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

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.

Original language	English
Title of host publication	Artificial Intelligence and Robotics
Editors	Xing Xu, Huimin Lu
Publisher	Springer Verlag
Pages	213-221
Number of pages	9
ISBN (Print)	9783319698762
DOIs	https://doi.org/10.1007/978-3-319-69877-9_23
Publication status	Published - Jan 1 2018
Event	2nd International Symposium on Artificial Intelligence and Robotics, ISAIR 2017 - Kitakyushu, Japan Duration: Nov 25 2017 → Nov 26 2017

Publication series

Name	Studies in Computational Intelligence
Volume	752
ISSN (Print)	1860-949X

Other

Other	2nd International Symposium on Artificial Intelligence and Robotics, ISAIR 2017
Country	Japan
City	Kitakyushu
Period	11/25/17 → 11/26/17

Fingerprint

All Science Journal Classification (ASJC) codes

Artificial Intelligence

Cite this

Chen, H., Trouve, A., Murakami, K. J., & Fukuda, A. (2018). A Concise Conversion Model for Improving the RDF Expression of ConceptNet Knowledge Base. In X. Xu, & H. Lu (Eds.), Artificial Intelligence and Robotics (pp. 213-221). (Studies in Computational Intelligence; Vol. 752). Springer Verlag. https://doi.org/10.1007/978-3-319-69877-9_23

A Concise Conversion Model for Improving the RDF Expression of ConceptNet Knowledge Base. / Chen, Hua; Trouve, Antoine; Murakami, Kazuaki J.; Fukuda, Akira.

Artificial Intelligence and Robotics. ed. / Xing Xu; Huimin Lu. Springer Verlag, 2018. p. 213-221 (Studies in Computational Intelligence; Vol. 752).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Chen, H, Trouve, A, Murakami, KJ & Fukuda, A 2018, A Concise Conversion Model for Improving the RDF Expression of ConceptNet Knowledge Base. in X Xu & H Lu (eds), Artificial Intelligence and Robotics. Studies in Computational Intelligence, vol. 752, Springer Verlag, pp. 213-221, 2nd International Symposium on Artificial Intelligence and Robotics, ISAIR 2017, Kitakyushu, Japan, 11/25/17. https://doi.org/10.1007/978-3-319-69877-9_23

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Access to Document

10.1007/978-3-319-69877-9_23