TY - GEN
T1 - A Concise Conversion Model for Improving the RDF Expression of ConceptNet Knowledge Base
AU - Chen, Hua
AU - Trouve, Antoine
AU - Murakami, Kazuaki J.
AU - Fukuda, Akira
PY - 2018/1/1
Y1 - 2018/1/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85033708328&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85033708328&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-69877-9_23
DO - 10.1007/978-3-319-69877-9_23
M3 - Conference contribution
AN - SCOPUS:85033708328
SN - 9783319698762
T3 - Studies in Computational Intelligence
SP - 213
EP - 221
BT - Artificial Intelligence and Robotics
A2 - Xu, Xing
A2 - Lu, Huimin
PB - Springer Verlag
T2 - 2nd International Symposium on Artificial Intelligence and Robotics, ISAIR 2017
Y2 - 25 November 2017 through 26 November 2017
ER -