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Multi-source knowledge integration based on machine learning algorithms for domain ontology

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Abstract

In this paper, a new approach of automatic building for domain ontology based on machine learning algorithm is proposed, and by which the large-scale e-Gov ontology is built automatically. The advent of the knowledge graph era puts forward higher requirements for semantic search and analysis. Since traditional manual ontology construction requires the participation of domain experts in large-scale ontology construction, which will take time and considerable resources, and the ontology scale is also limited. The approach proposed in this paper not only makes up for the shortage of thesaurus description of the semantic relation between terms, but also takes advantage of the massive online encyclopedia knowledge and typical similarity algorithm in machine learning to fill the domain ontology automatically, so that the advantages of the two different knowledge sources are fully utilized and the system as a whole is gained. Ultimately, this may provide the foundation and support for the construction of knowledge graph and the semantic-oriented applications.

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Notes

  1. See http://ir.hit.edu.cn/demo/ltp/Sharing_Plan.htm.

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Acknowledgements

This work was supported by the Scientific Research Project of Beijing Municipal Education Commission (General Social Science Project) and the Youth Excellent Teachers Grant of Capital University of Economics and Business (No. 23491854840429).

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Authors and Affiliations

  1. Information School, Capital University of Economics and Business, Beijing, 100070, China

    Ting Wang, Hanzhe Gu, Zhuang Wu & Jing Gao

  2. CTSC Center, Information School, Capital University of Economics and Business, Beijing, 100070, China

    Zhuang Wu

Authors
  1. Ting Wang
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  2. Hanzhe Gu
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  3. Zhuang Wu
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  4. Jing Gao
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Correspondence to Ting Wang.

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Wang, T., Gu, H., Wu, Z. et al. Multi-source knowledge integration based on machine learning algorithms for domain ontology. Neural Comput & Applic 32, 235–245 (2020). https://doi.org/10.1007/s00521-018-3806-5

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