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Resource Propagation Algorithm to Reinforce Knowledge Base in Linked Data

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Advances in Network-Based Information Systems (NBiS 2017)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 7))

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Abstract

Linked Data are data of directed graph with labels to describe based on Resource Description Framework (RDF), and can create a knowledge base by linking each resource on the Web. However, a large amount of Linked Data does not have enough links since resources defined at Internationalized Resource Identifier (IRI) data type are scanty. Therefore, this paper presents that Resource Propagation Algorithm predicts links between resources in Linked Data based on semantic distance, and reinforces the knowledge base. The algorithm was confirmed that it was able to generate semantic links between the resources considering predicate dictionary.

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Acknowledgement

This work was supported by JSPS KAKENHI Grant Number 17J09765 for Resource Fellowship for Young Scientists.

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Authors and Affiliations

  1. Intelligent Information System Engineering, Fukuoka Institute of Technology, Fukuoka, Japan

    Toshitaka Maki, Kazuki Takahashi & Toshihiko Wakahara

  2. College of Policy Studies, Tsuda University, Kodaira, Japan

    Akihisa Kodate & Noboru Sonehara

Authors
  1. Toshitaka Maki
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  2. Kazuki Takahashi
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  3. Toshihiko Wakahara
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  4. Akihisa Kodate
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  5. Noboru Sonehara
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Corresponding author

Correspondence to Toshitaka Maki .

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Editors and Affiliations

  1. Department of Information and Communication Engineering, Faculty of Information Engineering, Fukuoka Institute of Technology, Fukuoka, Japan

    Leonard Barolli

  2. Rissho University, Tokyo, Japan

    Tomoya Enokido

  3. Hosei University, Tokyo, Japan

    Makoto Takizawa

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Maki, T., Takahashi, K., Wakahara, T., Kodate, A., Sonehara, N. (2018). Resource Propagation Algorithm to Reinforce Knowledge Base in Linked Data. In: Barolli, L., Enokido, T., Takizawa, M. (eds) Advances in Network-Based Information Systems. NBiS 2017. Lecture Notes on Data Engineering and Communications Technologies, vol 7. Springer, Cham. https://doi.org/10.1007/978-3-319-65521-5_41

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  • DOI: https://doi.org/10.1007/978-3-319-65521-5_41

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-65520-8

  • Online ISBN: 978-3-319-65521-5

  • eBook Packages: EngineeringEngineering (R0)

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