Sequence Encoder-based Spatiotemporal Knowledge Graph Completion

Authors

  • Wei Jia 1)State Key Laboratory of Air Traffic Management System and Technology, China 2)Nanjing University of Aeronautics and Astronautics, China
  • Xuan Wang State Key Laboratory of Air Traffic Management System and Technology, China
  • Jing Shan Nanjing University of Aeronautics and Astronautics, China
  • Li Yan Nanjing University of Aeronautics and Astronautics, China
  • Weinan Niu Nanjing University of Aeronautics and Astronautics, China
  • Zongmin Ma Nanjing University of Aeronautics and Astronautics, China

DOI:

https://doi.org/10.13052/jwe1540-9589.2166

Keywords:

Knowledge graph completion, recursive neural network, spatiotemporal information

Abstract

Knowledge graph (KG) completion aims to infer new facts from incomplete knowledge graphs. Most existing solutions focus on learning from time-aware fact triples and ignore the spatial information. In reality, knowledge graphs can evolve with time as well as the changing locations, such as the flight domain. Therefore, integrating spatiotemporal information into knowledge graph representation is important for the knowledge graph completion. To address this problem, this paper proposes two Spatio Temporal-aware knowledge graph completion models based on the Sequence Encoder, namely STSE and S-TSE, which incorporate the spatial and temporal information into relations. Specifically, the model consists of two steps: spatiotemporal-aware relation encoding and final scoring function evaluation. The first stage composes the spatiotemporal information into different tokens. Then two methods are proposed to obtain the embedding of spatiotemporal-aware relation by utilizing the Recursive Neural Network. The second stage proposes different scoring functions for two models. Empirically evaluation of the proposed models is conducted on spatiotemporal-aware KG completion task on two public datasets. Experimental results demonstrate the effectiveness of the proposal for spatiotemporal knowledge graph completion.

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Author Biographies

Wei Jia, 1)State Key Laboratory of Air Traffic Management System and Technology, China 2)Nanjing University of Aeronautics and Astronautics, China

Wei Jia received the M.S. degree in computer science from Shandong University of Science and Technology, China in 2020. She is currently working toward the Ph.D. degree in the School of Computer Science and Technology at Nanjing University of Aeronautics and Astronautics, China. Her fields of research are social network, knowledge graph, service computing and intelligent information systems.

Xuan Wang, State Key Laboratory of Air Traffic Management System and Technology, China

Xuan Wang received the B.E. degree from University of Hertfordshire, Hartfield, UK in 2011. Later on, he received his M.S. degree from Southampton University, Southampton, UK in 2012. He received his PhD degree in Electronics and Electrical Engineering from Southampton University in 2017. He is currently a senior engineer of State Key Laboratory of Air Traffic Management System and Technology, Nanjing, China. His research interests include information extraction and natural language understanding.

Jing Shan, Nanjing University of Aeronautics and Astronautics, China

Jing Shan received the bachelor degree in information security in 2010 and the master degree in computer science in 2013 from Nanjing University of Aeronautics and Astronautics, China. She is a research associate at Nanjing University of Aeronautics and Astronautics, China, engaging in intelligent system for task allocation, optimization algorithm and application of intelligent transportation system.

Li Yan, Nanjing University of Aeronautics and Astronautics, China

Li Yan received her PhD degree from Northeastern University, China. She is currently a full professor at Nanjing University of Aeronautics and Astronautics, China. Her research interests mainly include big data processing, knowledge graph, spatiotemporal data management, and fuzzy data modeling. She has published more than fifty papers on these topics. She is the author of three monographs published by Springer.

Weinan Niu, Nanjing University of Aeronautics and Astronautics, China

Weinan Niu received the M.S. degree in computer science from Shandong University of Science and Technology, China in 2020. He is currently working toward the Ph.D. degree in the School of Computer Science and Technology at Nanjing University of Aeronautics and Astronautics, China. His fields of research are social network, influence maximization, service computing and intelligent information systems.

Zongmin Ma, Nanjing University of Aeronautics and Astronautics, China

Zongmin Ma received his PhD degree from City University of Hong Kong, China. He is currently a full professor at Nanjing University of Aeronautics and Astronautics, China. His research interests include big data and knowledge engineering, the Semantic Web, temporal/spatial information modeling and processing, deep learning, and knowledge representation and reasoning with a special focus on information uncertainty. He has published more than two hundred papers in international journals and conferences on these topics. In addition, he (co-)authors six monographs with Springer. He is a Fellow of the IFSA and a senior member of the IEEE.

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Published

2022-11-09

How to Cite

Jia, W. ., Wang, X. ., Shan, J. ., Yan, L. ., Niu, W. ., & Ma, Z. . (2022). Sequence Encoder-based Spatiotemporal Knowledge Graph Completion. Journal of Web Engineering, 21(06), 1913–1936. https://doi.org/10.13052/jwe1540-9589.2166

Issue

2022: Vol 21 Iss 6

Section

Articles