skip to main content
10.1145/3502223.3502248acmotherconferencesArticle/Chapter ViewAbstractPublication PagesijckgConference Proceedingsconference-collections
research-article

Explainable Knowledge Reasoning Framework Using Multiple Knowledge Graph Embedding

Published:24 January 2022Publication History

ABSTRACT

Knowledge reasoning using knowledge graphs has attracted much attention. However, there is difficulty in integrating various related works to realize complex reasoning with explanation using multiple knowledge graphs. To do this, I propose a reasoning framework which combines multiple knowledge graph embedding techniques with corresponding explainable AI techniques. Experiments using the third knowledge graph reasoning challenge dataset demonstrate the effectiveness of the framework.

References

  1. [1] A. Bordes, N. Usunier, A. Garcia-Duran, J. Weston, and O. Yakhnenko. 2013. Translating embeddings for modeling multi-relational data. In Advances in neural information processing systems.Google ScholarGoogle Scholar
  2. [2] Y. Lin, Z. Liu, H. Luan, M. Sun, S. Rao, and S. Liu. 2015. Modeling Relation Paths for Representation Learning of Knowledge Bases. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, 705-714.Google ScholarGoogle Scholar
  3. [3] M. Schlichtkrull, T. N. Kipf, P. Bloem, R. van den Berg, I. Titov, and M. Welling. 2018. Modeling relational data with graph convolutional networks. In European Semantic Web Conference, 593-607.Google ScholarGoogle Scholar
  4. [4] B. Yang, W.-t. Yih, X. He, J. Gao, and L. Deng. 2015. Embedding entities and relations for learning and inference in knowledge bases. In Proceedings of the 3rd International Conference on Learning Representations.Google ScholarGoogle Scholar
  5. [5] H. Zhu, R. Xie, Z. Liu, and M. Sun. 2017. Iterative entity alignment via knowledge embeddings. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI).Google ScholarGoogle Scholar
  6. [6] A. C. Gusmao, A. H. C. Correia, G. De Bona, and F. G. Cozman. 2018. Interpreting Embedding Models of Knowledge Bases: A Pedagogical Approach. In ICML Workshop on Human Interpretability in Machine Learning (WHI 2018).Google ScholarGoogle Scholar
  7. [7] O. Biran, and C. Cotton. 2017. Explanation and justification in machine learning: A survey. In IJCAI-17 workshop on explainable AI (XAI).Google ScholarGoogle Scholar
  8. [8] M. T. Ribeiro, S. Singh, and C. Guestrin. 2016. Why should I trust you? Explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, 1135-1144.Google ScholarGoogle Scholar
  9. [9] R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra. 2017. Grad-CAM: Visual explanations from deep networks via gradient-based localization. In Proceedings of the IEEE international conference on computer vision, 618-626.Google ScholarGoogle Scholar
  10. [10] Z. Ying, D. Bourgeois, J. You, M. Zitnik, and J. Leskovec. 2019. GNNExplainer: Generating explanations for graph neural networks. In Advances in neural information processing systems.Google ScholarGoogle Scholar
  11. [11] M. Fuji, H. Morita, K. Goto, K. Maruhashi, H. Anai, and N. Igata. 2019. Explainable AI through combination of deep tensor and knowledge graph. Fujitsu Scientific and Technical Journal, 55(2), 58-64.Google ScholarGoogle Scholar
  12. [12] R. Speer, J. Chin, and C. Havasi. 2017. ConceptNet 5.5: An Open Multilingual Graph of General Knowledge. In Proceedings of the 31th AAAI.Google ScholarGoogle Scholar

Index Terms

  1. Explainable Knowledge Reasoning Framework Using Multiple Knowledge Graph Embedding
      Index terms have been assigned to the content through auto-classification.

      Recommendations

      Comments

      Login options

      Check if you have access through your login credentials or your institution to get full access on this article.

      Sign in
      • Published in

        cover image ACM Other conferences
        IJCKG '21: Proceedings of the 10th International Joint Conference on Knowledge Graphs
        December 2021
        204 pages

        Copyright © 2021 ACM

        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

        Publisher

        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 24 January 2022

        Permissions

        Request permissions about this article.

        Request Permissions

        Check for updates

        Qualifiers

        • research-article
        • Research
        • Refereed limited
      • Article Metrics

        • Downloads (Last 12 months)108
        • Downloads (Last 6 weeks)15

        Other Metrics

      PDF Format

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader

      HTML Format

      View this article in HTML Format .

      View HTML Format