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The concept information of graph granule with application to knowledge graph embedding

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Abstract

Knowledge graph embedding (KGE) has become one of the most effective methods for the numerical representation of entities and their relations in knowledge graphs. Traditional methods primarily utilise triple facts, structured as (head entity, relation, tail entity), as the basic knowledge units in the learning process and use additional external information to improve the performance of models. Since triples are sometimes less than adequate and external information is not always available, obtaining structured internal knowledge from knowledge graphs (KGs) naturally becomes a feasible method for KGE learning. Motivated by this, this paper employs formal concept analysis (FCA) to mine deterministic concept knowledge in KGs and proposes a novel KGE model by taking the concept information into account. More specifically, triples sharing the same head entity are organised into knowledge structures named graph granules, and then were transformed into concept lattices, based on which a novel lattice-based KGE model (TransGr) is proposed for knowledge graph completion. TransGr assumes that entities and relations exist in different granules and uses a matrix (obtained by fusing concepts from concept lattice) for quantitatively depicting the graph granule. Afterwards, it forces entities and relations to meet graph granule constraints when learning vector representations of KGs. Experiments on link prediction and triple classification demonstrated that the proposed TransGr is effective on the datasets with relatively complete graph granules.

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No datasets were generated or analysed during the current study.

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Acknowledgements

This work was supported by the Natural Science Foundation of Hubei Province (No. 2024AFB345) and the National Natural Science Foundation of China (No. 12071131).

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Author notes

  1. Degang Chen, Yinglong Ma and Jinhai Li contributed equally to this work.

Authors and Affiliations

  1. School of Computer Science, Yangtze University, Nanhuan Road, Jingzhou, 434020, Hubei Province, China

    Jiaojiao Niu

  2. School of Mathematics and Physics, North China Electric Power University, Beinong Road, Beijing, 102206, Beijing, China

    Degang Chen

  3. School of Control and Computer Engineering, North China Electric Power University, Beinong Road, Beijing, 102206, Beijing, China

    Yinglong Ma

  4. Data Science Research Center and the Faculty of Science, Kunming University of Science and Technology, Jingming South Road, Kunming, 650500, Yunnan Province, China

    Jinhai Li

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  1. Jiaojiao Niu
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Contributions

Jiaojiao Niu: software, data curation, writing—original draft preparation, visualization, funding acquisition. Degang Chen: writing—reviewing and editing, funding acquisition. Yinglong Ma: investigation, writing—reviewing and editing. Jinhai Li: supervision.

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Correspondence to Jiaojiao Niu.

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Niu, J., Chen, D., Ma, Y. et al. The concept information of graph granule with application to knowledge graph embedding. Int. J. Mach. Learn. & Cyber. 15, 5595–5606 (2024). https://doi.org/10.1007/s13042-024-02267-4

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  • DOI: https://doi.org/10.1007/s13042-024-02267-4

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