[1] Qiu Jingyi, Zhang Duxi, Song Aibo, Wang Honglin Zhang Tianbo, et al. Embedding-based approximate query for knowledge graph [J]. Journal of Southeast University (English Edition), 2024, 40 (4): 417-424. [doi:10.3969/j.issn.1003-7985.2024.04.011]

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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:

40

Issue:

2024 4

Page:

417-424

Research Field:

Computer Science and Engineering

Publishing date:

2024-12-03

Info

Title:

Embedding-based approximate query for knowledge graph

Author(s):

Qiu Jingyi¹;  Zhang Duxi²;  Song Aibo¹;  Wang Honglin³ Zhang Tianbo¹;  Jin Jiahui¹;  Fang Xiaolin¹;  Li Yaqi¹

¹ School of Computer Science and Engineering, Southeast University, Nanjing 211189, China
² Ningbo Power Supply Co., State Grid Zhejiang Electric Power Co., Ltd., Ningbo 315000, China
³ School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing 210044, China

Keywords:

approximate query;  knowledge graph;  embedding;  deep neural network

PACS:

TP392

DOI:

10.3969/j.issn.1003-7985.2024.04.011

Abstract:

To solve the low efficiency of approximate queries caused by the large sizes of the knowledge graphs in the real world, an embedding-based approximate query method is proposed. First, the nodes in the query graph are classified according to the degrees of approximation required for different types of nodes. This classification transforms the query problem into three constraints, from which approximate information is extracted. Second, candidates are generated by calculating the similarity between embeddings. Finally, a deep neural network model is designed, incorporating a loss function based on the high-dimensional ellipsoidal diffusion distance. This model identifies the distance between nodes using their embeddings and constructs a score function. k nodes are returned as the query results. The results show that the proposed method can return both exact results and approximate matching results. On datasets DBLP(DataBase systems and Logic Programming)and FUA-S(Flight USA Airports-Sparse), this method exhibits superior performance in terms of precision and recall, returning results in 0.10 and 0.03 s, respectively. This indicates greater efficiency compared to PathSim and other comparative methods.

References:

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Memo

Memo:

Biographies: Qiu Jingyi(1996—), female, Ph.D. candidate; Song Aibo(corresponding author), male, doctor, professor, absong@seu.edu.cn.
Foundation item: The State Grid Technology Project(No. 5108202340042A-1-1-ZN).
Citation: Qiu Jingyi, Zhang Duxi, Song Aibo, et al.Embedding-based approximate query for knowledge graph[J].Journal of Southeast University(English Edition), 2024, 40(4):417-424.DOI:10.3969/j.issn.1003-7985.2024.04.011.

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