Knowledge Graph Summarization

Knowledge Graph Summarization is a computational method that belongs to the class of knowledge graph abstraction techniques, distinguished by its purpose of generating a concise and meaningful representation of a knowledge graph by retaining salient entities, relations, and structural patterns while reducing redundancy and complexity.

28

Publications

1.2K

Citations

103

Authors

63

Institutions

Publications per year

2017–2026

21

Authors

103

  1. GC
    Gong Cheng

    Nanjing University, China

    3 publications · 50 citations

    3

    h-index

  2. KG
    Kalpa Gunaratna

    Samsung (United States), United States

    2 publications · 35 citations

    2

    h-index

  3. IM
    Ioana Manolescu

    Institut national de recherche en sciences et technologies du numérique, France

    2 publications · 82 citations

    2

    h-index

  4. QS
    Qi Song

    Washington State University, United States

    2 publications · 118 citations

    2

    h-index

  5. AS
    Amit Sheth

    Wright State University, United States

    2 publications · 54 citations

    2

    h-index

1–5 of 103

Venues

  1. The VLDB Journal

    3 publications · 94 citations

    3

    h-index

  2. Proceedings of the VLDB Endowment

    PVLDB · 2 publications · 138 citations

    2

    h-index

  3. Journal of Intelligent Information Systems

    2 publications · 98 citations

    2

    h-index

  4. Semantic Web

    2 publications · 44 citations

    2

    h-index

  5. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 1 publications · 92 citations

    1

    h-index

1–5