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

Knowledge Graph Completion Method of Combining Structural Information with Semantic Information

doi: 10.23919/cje.2022.00.299
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  • Author Bio:

    Binhao HU was born in 1996. He received the B.E. degree in Electronic Engineering from Sichuan University. He is a M.S. candidate of Zhengzhou University. His research interests include graph representation, knowledge graph and NLP.(Email: hu15181620732@163.com)

    Jianpeng ZHANG (Ph.D, Eindhoven University of Technology, 2018) is an Assistant Professor in the National Digital Switching System Engineering & Technological R&D Center (NDSC), 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 National Digital Switching System Engineering and Technological Research Center. 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
  • 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 and 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 rep-resenting triples as low-dimensional dense vectors. However, is difficult to generalize to unseen entities, Entities that are not observed during training but appear during testing. The 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, but 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 then use a hyperbolic space-based distance model to learn structural information and integrate the two types of information together. We evaluate our model by performing link prediction experi-ments in standard datasets. In experiments, our model achieves better performances than state-of-the-art meth-ods on two standard datasets.
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