Knowledge Graph Completion Method of Combining Structural Information with Semantic Information

HU Binhao; ZHANG Jianpeng; CHEN Hongchang

doi:10.23919/cje.2022.00.299

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Binhao HU, Jianpeng ZHANG, and Hongchang CHEN, “Knowledge Graph Completion Method of Combining Structural Information with Semantic Information,” Chinese Journal of Electronics, vol. 33, no. 6, pp. 1–9, 2024 doi: 10.23919/cje.2022.00.299

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Binhao HU, Jianpeng ZHANG, and Hongchang CHEN, “Knowledge Graph Completion Method of Combining Structural Information with Semantic Information,” Chinese Journal of Electronics, vol. 33, no. 6, pp. 1–9, 2024 doi: 10.23919/cje.2022.00.299

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Knowledge Graph Completion Method of Combining Structural Information with Semantic Information

doi: 10.23919/cje.2022.00.299

1.
School of Cyber Science and Engineering, Zhengzhou University, Zhengzhou 450002, China
2.
National Digital Switching System Engineering & Technological R&D Center (NDSC), Zhengzhou 450002, China

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Author Bio:
Binhao HU was born in 1996. He received the B.E. degree in electronic engineering from Sichuan University, Chengdu, China. He is currently a M.S. candidate of Zhengzhou University, Zhengzhou, China. His research interests include graph representation, knowledge graph and natural language processing.(Email: hu15181620732@163.com)

Jianpeng ZHANG received the Ph.D. degree from Eindhoven University of Technology, Eindhoven, Netherlands, in 2018. Now he is an Assistant Professor with the National Digital Switching System Engineering & Technological R&D Center, Zhengzhou, China. His research interests include data mining, big data analytics and social network analysis.(Email: zjp@ndsc.com.cn)

Hongchang CHEN is currently a Professor at the National Digital Switching System Engineering & Technological R&D Center, Zhengzhou, China. His research interests include digital society governance and data mining.(Email: chenhongchang@ndsc.com.cn)
Corresponding author: Email: zjp@ndsc.com.cn
Received Date: 2022-09-02
Accepted Date: 2023-12-19

Available Online: 2024-03-06

Abstract

Abstract

With the development of knowledge graphs, a series of applications based on knowledge graphs have emerged. The incompleteness of knowledge graphs makes the effect of the downstream applications affected by the quality of the knowledge graphs. To improve the quality of knowledge graphs, translation-based graph embeddings such as TransE, learn structural information by representing triples as low-dimensional dense vectors. However, it is difficult to generalize to the unseen entities that are not observed during training but appear during testing. Other methods use the powerful representational ability of pre-trained language models to learn entity descriptions and contextual representation of triples. Although they are robust to incompleteness, they need to calculate the score of all candidate entities for each triple during inference. We consider combining two models to enhance the robustness of unseen entities by semantic information, and prevent combined explosion by reducing inference overhead through structured information. We use a pre-training language model to code triples and learn the semantic information within them, and use a hyperbolic space-based distance model to learn structural information, then integrate the two types of information together. We evaluate our model by performing link prediction experiments on standard datasets. The experimental results show that our model achieves better performances than state-of-the-art methods on two standard datasets.
- Knowledge graph,
- Knowledge graph completion,
- Representation learning

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References(27)

References

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