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Ranking Diagnoses for Inconsistent Knowledge Graphs by Representation Learning

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Semantic Technology (JIST 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11341))

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Abstract

When a knowledge graph (KG) is growing e.g. by knowledge graph completion, it might become inconsistent with the logical theory which formalizing the schema of the KG. A common approach to restoring consistency is removing a minimal set of triples from the KG, called a diagnosis of the KB. However, there can be a large number of diagnoses. It is hard to manually select the best one among these diagnoses to restore consistency. To alleviate the selection burden, this paper studies automatic methods for ranking diagnoses so that people can merely focus on top diagnoses when seeking the best one. An approach to ranking diagnoses through representation learning aka knowledge graph embedding is proposed. Given a set of diagnoses, the approach first learns the embedding of the complement set of the union of all diagnoses, then for every diagnosis, incrementally learns an embedding of the complement set of the diagnosis and employs the embedding to estimate the removal cost of the diagnosis, and finally ranks diagnoses by removal costs. To evaluate the approach, four knowledge graphs with logical theories are constructed from the four great classical masterpieces of Chinese literature. Experimental results on these datasets show that the proposed approach is significantly more effective than classical random methods in ranking the best diagnoses at top places.

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Notes

  1. 1.

    https://www.rapidminer.com/.

  2. 2.

    https://protege.stanford.edu/.

  3. 3.

    http://www.openkg.cn/tool/rank-diagnoses.

  4. 4.

    In fact it is guaranteed true for DRC, OM and RTK, since for each of these knowledge graphs the training set is the complement set of the union of all diagnoses.

  5. 5.

    The computation of all MISs in a Horn KB is already intractable.

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Acknowledgements

This work was partly supported by National Natural Science Foundation of China (61375056 and 61876204), Science and Technology Program of Guangzhou (201804010496), and Scientific Research Innovation Team in Department of Education of Guangdong Province (2017KCXTD013).

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Authors and Affiliations

  1. Guangdong University of Foreign Studies, Guangzhou, 510006, China

    Jianfeng Du

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  1. Jianfeng Du
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Correspondence to Jianfeng Du .

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Editors and Affiliations

  1. National Institute of Informatics, Tokyo, Japan

    Ryutaro Ichise

  2. Accenture Labs, Dublin, Ireland

    Freddy Lecue

  3. Japan Science and Technology Agency, Tokyo, Japan

    Takahiro Kawamura

  4. Peking University, Beijing, China

    Dongyan Zhao

  5. Imperial College London, London, UK

    Stephen Muggleton

  6. Osaka University, Osaka, Japan

    Kouji Kozaki

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Du, J. (2018). Ranking Diagnoses for Inconsistent Knowledge Graphs by Representation Learning. In: Ichise, R., Lecue, F., Kawamura, T., Zhao, D., Muggleton, S., Kozaki, K. (eds) Semantic Technology. JIST 2018. Lecture Notes in Computer Science(), vol 11341. Springer, Cham. https://doi.org/10.1007/978-3-030-04284-4_4

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  • DOI: https://doi.org/10.1007/978-3-030-04284-4_4

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  • Online ISBN: 978-3-030-04284-4

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