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ERDERP: Entity and Relation Double Embedding on Relation Hyperplanes and Relation Projection Hyperplanes

Author

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  • Lin Lin

    (School of Mechatronics Engineering, Harbin Institute of Technology, 92 Xidazhi Road, Harbin 150001, China)

  • Jie Liu

    (School of Mechatronics Engineering, Harbin Institute of Technology, 92 Xidazhi Road, Harbin 150001, China)

  • Feng Guo

    (School of Mechatronics Engineering, Harbin Institute of Technology, 92 Xidazhi Road, Harbin 150001, China)

  • Changsheng Tong

    (School of Mechatronics Engineering, Harbin Institute of Technology, 92 Xidazhi Road, Harbin 150001, China)

  • Lizheng Zu

    (School of Mechatronics Engineering, Harbin Institute of Technology, 92 Xidazhi Road, Harbin 150001, China)

  • Hao Guo

    (School of Mechatronics Engineering, Harbin Institute of Technology, 92 Xidazhi Road, Harbin 150001, China)

Abstract

Since data are gradually enriched over time, knowledge graphs are inherently imperfect. Thus, knowledge graph completion is proposed to perfect knowledge graph by completing triples. Currently, a family of translation models has become the most effective method for knowledge graph completion. These translation models are modeled to solve the complexity and diversity of entities, such as one-to-many, many-to-one, and many-to-many, which ignores the diversity of relations themselves, such as multiple relations between a pair of entities. As a result, with current translation models, it is difficult to effectively extract the semantic information of entities and relations. To effectively extract the semantic information of the knowledge graph, this paper fundamentally analyzes the complex relationships of the knowledge graph. Then, considering the diversity of relations themselves, the complex relationships are refined as one-to-one-to-many, many-to-one-to-one, one-to-many-to-one, many-to-one-to-many, many-to-many-to-one, one-to-many-to-many, and many-to-many-to-many. By analyzing the complex relationships, a novel knowledge graph completion model, entity and relation double embedding on relation hyperplanes and relation projection hyperplanes (ERDERP), is proposed to extract the semantic information of entities and relations. First, ERDERP establishes a relation hyperplane for each relation and projects the relation embedding into the relation hyperplane. Thus, the semantic information of the relations is extracted effectively. Second, ERDERP establishes a relation projection hyperplane for each relation projection and projects entities into relation projection hyperplane. Thus, the semantic information of the entities is extracted effectively. Moreover, it is theoretically proved that ERDERP can solve antisymmetric problems. Finally, the proposed ERDERP are compared with several typical knowledge graph completion models. The experimental results show that ERDERP is significantly effective in link prediction, especially in relation prediction. For instance, on FB15k and FB15k-237, Hits@1 of ERDERP outperforms TransH at least 30%.

Suggested Citation

  • Lin Lin & Jie Liu & Feng Guo & Changsheng Tong & Lizheng Zu & Hao Guo, 2022. "ERDERP: Entity and Relation Double Embedding on Relation Hyperplanes and Relation Projection Hyperplanes," Mathematics, MDPI, vol. 10(22), pages 1-19, November.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:22:p:4182-:d:967203
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    References listed on IDEAS

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    1. Fu, Song & Zhang, Yongjian & Lin, Lin & Zhao, Minghang & Zhong, Shi-sheng, 2021. "Deep residual LSTM with domain-invariance for remaining useful life prediction across domains," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    2. Miaoyuan Shi, 2021. "Knowledge Graph Question and Answer System for Mechanical Intelligent Manufacturing Based on Deep Learning," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-8, February.
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