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Uncertain Ontology-Aware Knowledge Graph Embeddings

  • Khaoula BoutouhamiEmail author
  • Jiatao Zhang
  • Guilin QiEmail author
  • Huan Gao
Conference paper
  • 61 Downloads
Part of the Communications in Computer and Information Science book series (CCIS, volume 1157)

Abstract

Much attention has recently been given to knowledge graphs embedding by exploiting latent and semantic relations among entities and incorporating the structured knowledge they contain into machine learning. Most of the existing graph embedding models can only encode a simple model of the data, while few models are designed for ontology rich knowledge graphs. Furthermore, many automated knowledge construction tools produce modern knowledge graphs with rich semantics and uncertainty. However, there is no graph embedding model which includes uncertain ontological information into graph embedding models. In this paper, we propose a novel embedding model UOKGE (Uncertain Ontology-aware Knowledge Graph Embeddings), which learns embeddings of entities, classes, and properties on uncertain ontology-aware knowledge graphs according to confidence scores. The proposed method preserves both structures and uncertainty of knowledge in the embedding space. Specifically, UOKGE encodes each entity in a knowledge graph as a point of n-dimensional vector, each class as a n-sphere and each property as 2n-sphere in the same semantic space. This representation allows for the natural expression of uncertain ontological triples. The preliminary experimental results show that UOKGE can robustly learn representations of uncertain ontology-aware knowledge graphs when evaluated on a benchmark dataset.

Keywords

Ontology-aware knowledge graph Knowledge graphs Embedding Uncertainty 

Notes

Acknowledgements

Research presented in this paper was partially supported by the National Key Research and Development Program of China under grants (2018YFC0830200, 2017YFB1002801), the Natural Science Foundation of China grants (U1736204).

References

  1. 1.
    Bordes, A., Usunier, N., Garcia-Duran, A., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multi-relational data. In: Proceedings of NIPS, pp. 2787–2795 (2013)Google Scholar
  2. 2.
    Chen, X., Chen, M., Shi, W., Sun, Y., Zaniolo, C.: Embedding uncertain knowledge graphs. In: Proceedings of AAAI, pp. 3363–3370 (2019)CrossRefGoogle Scholar
  3. 3.
    Diaz, G.I., Fokoue, A., Sadoghi, M.: EmbedS: scalable, ontology-aware graph embeddings. In: Proceedings of EDBT, pp. 433–436 (2018)Google Scholar
  4. 4.
    Dubois, D., Prade, H.: Possibility theory, probability theory and multiple-valued logics: a clarification. Ann. Math. Artif. Intell. 32(1–4), 35–66 (2001).  https://doi.org/10.1023/A:1016740830286MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Ji, G., He, S., Xu, L., Liu, K., Zhao, J.: Knowledge graph embedding via dynamic mapping matrix. In: Proceedings of ACL, pp. 687–696 (2015)Google Scholar
  6. 6.
    Hu, J., Cheng, R., Huang, Z., Fang, Y., Luo, S.: On embedding uncertain graphs. In: Proceedings of CIKM, pp. 157–166 (2017)Google Scholar
  7. 7.
    Kallenberg, O.: Foundations of modern probability. Springer, New York (2006).  https://doi.org/10.1007/b98838CrossRefzbMATHGoogle Scholar
  8. 8.
    Lin, Y., Liu, Z., Sun, M., Liu, Y., Zhu, X.: Learning entity and relation embeddings for knowledge graph completion. In: Proceedings of AAAI (2015)Google Scholar
  9. 9.
    Lv, X., Hou, L., Li, J., Liu, Z.: Differentiating concepts and instances for knowledge graph embedding. In: Proceedings of EMNLP, pp. 1971–1979 (2018)Google Scholar
  10. 10.
    Trouillon, T., Welbl, J., Riedel, S., Gaussier, É., Bouchard, G.: Complex embeddings for simple link prediction. In: Proceedings of ICML, pp. 2071–2080 (2016)Google Scholar
  11. 11.
    Wang, Q., Mao, Z., Wang, B., Guo, L.: Knowledge graph embedding: a survey of approaches and applications. IEEE Trans. Knowl. Data Eng. 29(12), 2724–2743 (2017)CrossRefGoogle Scholar
  12. 12.
    Wang, Z., Zhang, J., Feng, J., Chen, Z.: Knowledge graph embedding by translating on hyperplanes. In: Proceedings of AAAI (2014)Google Scholar
  13. 13.
    Zadeh, L.A.: Fuzzy sets. Inf. Control 8(3), 338–353 (1965)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.School of Computer Science and EngineeringSoutheast UniversityNanjingChina

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