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Identification of the Conjugate Pair to Estimating Object Distance: An Application of the Ant Colony Algorithm Read chapter Read first chapter

2018 | OriginalPaper | Chapter

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

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

Published in: Artificial Intelligence and Robotics

Publisher: Springer International Publishing

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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.

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Footnotes
2
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Metadata
Title
A Concise Conversion Model for Improving the RDF Expression of ConceptNet Knowledge Base
Authors
Hua Chen
Antoine Trouve
Kazuaki J. Murakami
Akira Fukuda
Copyright Year
2018
Book
Artificial Intelligence and Robotics

Print ISBN: 978-3-319-69876-2

Electronic ISBN: 978-3-319-69877-9

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-69877-9_23

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