Relational Triple Extraction

Relational Triple Extraction is a natural language processing task that identifies and extracts semantically meaningful triples from unstructured text, where each triple consists of a subject entity, a relation, and an object entity, thereby structuring relational knowledge for downstream applications.

33

Publications

2.2K

Citations

114

Authors

46

Institutions

Publications per year

2017–2026

27

Authors

114

  1. NZ
    Ningyu Zhang

    Zhejiang University, China

    4 publications · 313 citations

    4

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  2. YZ
    Yunyan Zhang

    Chinese Academy of Sciences, China

    2 publications · 250 citations

    2

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  3. HC
    Huajun Chen

    Zhejiang University, China

    2 publications · 63 citations

    2

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  4. LZ
    Longhui Zhang

    Northeastern University, China

    2 publications · 140 citations

    2

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  5. ZZ
    Ziheng Zhang

    Tencent (China), China

    2 publications · 250 citations

    2

    h-index

1–5 of 114

Venues

  1. arXiv (Cornell University)

    Repository · 3 publications · 75 citations

    3

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  2. Computers & Security

    1 publications · 57 citations

    1

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  3. IEEE Access

    1 publications · 13 citations

    1

    h-index

  4. 1

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  5. 1

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1–5