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Artificial Intelligence and Robotics pp 213–221Cite as

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

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 752))

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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Notes

  1. 1.

    Refer to http://score.cs.wayne.edu/result/.

  2. 2.

    As of 2017, OpenCyc is no longer available, details can refer to http://www.opencyc.org.

  3. 3.

    A predefined property, refer to http://www.w3.org/TR/rdf-schema/.

  4. 4.

    http://conceptnet5.media.mit.edu/downloads/current/.

References

  1. Aghaei, S., Nematbakhsh, M.A., Farsani, H.K.: Evolution of the World Wide Web: from Web 1.0 to Web 4.0. Int. J. Web & Semant. Technol. 3(1), 1–3 (2012)

    Google Scholar 

  2. Manola, F., Miller, E., McBride, B.: Resource description framework (RDF) primer. W3C Recomm. (2004)

    Google Scholar 

  3. Brickley, D., Guha, R.V.: RDF Vocabulary description language 1.0: RDF schema. W3C Recomm. (2004). https://www.w3.org/TR/rdf-schema

  4. Liu, H., Singh, P.: ConceptNet—a practical commonsense reasoning tool-kit. BT Technol. J. 22(4), 211–226 (2004)

    Article  Google Scholar 

  5. Lu, H., Li, Y., Chen, M., Kim, H., Serikawa, S.: Brain Intelligence: Go Beyond Artificial Intelligence. arXiv preprint (2017)

    Google Scholar 

  6. Najmi, E., Malik, Z., Hashmi, K., Rezgui, A.: ConceptRDF: An RDF presentation of ConceptNet knowledge base. In: 7th International Conference on Information and Communication Systems (ICICS), pp. 145–150. IEEE (2016)

    Google Scholar 

  7. Xu, X., He, L., Lu, H., Shimada, A., Taniguchi, R.I.: Non-linear matrix completion for social image tagging. IEEE Access 5, 6688–6696 (2017)

    Article  Google Scholar 

  8. Xu, X., He, L., Shimada, A., Taniguchi, R.I., Lu, H.: Learning unified binary codes for cross-modal retrieval via latent semantic hashing. Neurocomputing 213, 191–203 (2016)

    Article  Google Scholar 

  9. Crockford, D.: The Application/Json Media Type for Javascript Object Notation (json) (2006)

    Google Scholar 

  10. Grassi, M., Piazza, F.: Towards an RDF encoding of ConceptNet. In: International Symposium on Neural Networks, pp. 558–565. Springer, Berlin (2011)

    Google Scholar 

  11. Najmi, E., Hashmi, K., Malik, Z., Rezgui, A., Khanz, H.U.: ConceptOnto: an upper ontology based on conceptnet. In: 11th International Conference on Computer Systems and Applications (AICCSA), pp. 366–372. IEEE (2014)

    Google Scholar 

  12. Lenat, D.B.: CYC: A large-scale investment in knowledge infrastructure. Commun. ACM 38(11), 33–38 (1995)

    Article  Google Scholar 

  13. Prud, E., Seaborne, A.: SPARQL Query Language for RDF (2006)

    Google Scholar 

  14. Bollacker, K., Evans, C., Paritosh, P., Sturge, T., Taylor, J.: Freebase: A collaboratively created graph database for structuring human knowledge. In: The 2008 ACM SIGMOD International Conference on Management of Data, pp. 1247–1250. ACM (2008)

    Google Scholar 

  15. Auer, S., Bizer, C., Kobilarov, G., Lehmann, J., Cyganiak, R., Ives, Z.: DBpedia: A Nucleus for a Web of Open Data. The Semantic web, pp. 722–735 (2007)

    Google Scholar 

  16. Suchanek, F.M., Kasneci, G., Weikum, G.: Yago: A core of semantic knowledge. In: 16th International Conference on World Wide Web, pp. 697–706. ACM Press, USA (2007)

    Google Scholar 

  17. Miller, G.A.: WordNet: a Lexical database for English. Commun. ACM 38(11), 39–41 (1995)

    Article  Google Scholar 

  18. Speer, R., Havasi, C.: Representing general relational knowledge in ConceptNet 5. In: LREC, pp. 3679–3686 (2012)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Graduate School and Faculty of ISEE, Kyushu University, Fukuoka, Japan

    Hua Chen, Kazuaki J. Murakami & Akira Fukuda

  2. Team AIBOD Co., Ltd., Fukuoka, Japan

    Antoine Trouve & Kazuaki J. Murakami

Authors
  1. Hua Chen
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  2. Antoine Trouve
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  3. Kazuaki J. Murakami
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  4. Akira Fukuda
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Corresponding author

Correspondence to Hua Chen .

Editor information

Editors and Affiliations

  1. Department of Mechanical and Control Engineering, Kyushu Institute of Technology, Kitakyushu, Japan

    Huimin Lu

  2. School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, China

    Xing Xu

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

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

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-69876-2

  • Online ISBN: 978-3-319-69877-9

  • eBook Packages: EngineeringEngineering (R0)

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