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A Temporal Knowledge Graph Modeling Method Based on Temporal-RDF for Entity Alignment

Authors:

Lin Zhu,

Nan Li,

Luyi BaiAuthors Info & Claims

ACM-TURC '24: Proceedings of the ACM Turing Award Celebration Conference - China 2024

Pages 205 - 206

https://doi.org/10.1145/3674399.3674465

Published: 30 July 2024 Publication History

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Abstract

To address the problems of data redundancy and non-standardization of temporal information caused by the addition of extra labels in existing temporal knowledge graph (TKG) modeling methods, this paper proposes a TKG model based on Temporal-RDF. We serialize predicates and temporal information and merge them to construct new temporal predicates, thereby retaining the triple form of RDF while avoiding the generation of redundant labels and achieving non-manual standardization of temporal information. The proposed TKG model not only resolves the above problems but facilitates subsequent entity alignment task for TKGs.

References

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[1] Dai Quoc Nguyen, Thanh Vu, Tu Dinh Nguyen, and Dinh Q. Phung. Quatre: Relation-aware quaternions for knowledge graph embeddings. In Companion of The Web Conference 2022, pages 189–192. ACM, 2022.

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[2] Thanh Le, Nam Le, and Bac Le. Knowledge graph embedding by relational rotation and complex convolution for link prediction. Expert Syst. Appl., 214:119122, 2023.

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[3]

[3] Lin Zhu, Nan Li, Luyi Bai, Yunqing Gong, and Yizong Xing. strdfs: Spatiotemporal knowledge graph modeling. IEEE Access, 8:129043–129057, 2020.

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[4]

[4] Luyi Bai, Nan Li, Guishun Li, Ziyi Zhang, and Lin Zhu. Embedding-based entity alignment of cross-lingual temporal knowledge graphs. Neural Networks, 172:106143, 2024.

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Index Terms

A Temporal Knowledge Graph Modeling Method Based on Temporal-RDF for Entity Alignment
1. Information systems
  1. Data management systems
    1. Database design and models
      1. Data model extensions
        Temporal data

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Published In

ACM-TURC '24: Proceedings of the ACM Turing Award Celebration Conference - China 2024

July 2024

261 pages

ISBN:9798400710117

DOI:10.1145/3674399

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

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Published: 30 July 2024

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ACM-TURC '24

ACM-TURC '24: ACM Turing Award Celebration Conference 2024

July 5 - 7, 2024

Changsha, China

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Abstract

References

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Recommendations

Enhancing Knowledge Graph Attention by Temporal Modeling for Entity Alignment with Sparse Seeds

Temporal RDF

RDF, Jena, SparQL and the 'Semantic Web'

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