A Survey on Extractive Knowledge Graph Summarization: Applications, Approaches, Evaluation, and Future Directions
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Abstract (arXiv)
With the continuous growth of large Knowledge Graphs (KGs), extractive KG summarization becomes a trending task. Aiming at distilling a compact subgraph with condensed information, it facilitates various downstream KG-based tasks. In this survey paper, we are among the first to provide a systematic overview of its applications and define a taxonomy for existing methods from its interdisciplinary studies. Future directions are also laid out based on our extensive and comparative review.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 7 of its 50 references that are in the map. First posted on arXiv 19 Feb 2024.
Builds on
7 of 50 references.
References · 7
- 2020Knowledge Graphscited by 621
- 2023Large Language Models and Knowledge Graphs: Opportunities and Challengescited by 71
- 2019Dataset search: a surveycited by 36
- 2017The Odyssey Approach for Optimizing Federated SPARQL Queriescited by 5
- 2019Entity Summarization: State of the Art and Future Challengescited by 5
- 2020Enriching Documents with Compact, Representative, Relevant Knowledge Graphscited by 3
- 2019Towards More Usable Dataset Search: From Query Characterization to Snippet Generationcited by 2
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Citing papers per year: 2026: 1.
1 paper.