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

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

Research output: Chapter in Book/Report/Conference proceedingConference 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 languageEnglish
Title of host publicationArtificial Intelligence and Robotics
EditorsXing Xu, Huimin Lu
PublisherSpringer Verlag
Pages213-221
Number of pages9
ISBN (Print)9783319698762
DOIs
Publication statusPublished - Jan 1 2018
Event2nd International Symposium on Artificial Intelligence and Robotics, ISAIR 2017 - Kitakyushu, Japan
Duration: Nov 25 2017Nov 26 2017

Publication series

NameStudies in Computational Intelligence
Volume752
ISSN (Print)1860-949X

Other

Other2nd International Symposium on Artificial Intelligence and Robotics, ISAIR 2017
CountryJapan
CityKitakyushu
Period11/25/1711/26/17

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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