A Survey on Extractive Knowledge Graph Summarization: Applications, Approaches, Evaluation, and Future Directions

Xiaxia Wang, Gong Cheng
2/19/2024
cs.AIcs.DBcs.IR
survey

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Abstract

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.

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Cite this paper

@article{wang2024survey,
  title  = {A Survey on Extractive Knowledge Graph Summarization: Applications, Approaches, Evaluation, and Future Directions},
  author = {Xiaxia Wang and Gong Cheng},
  year   = {2024},
  eprint = {2402.12001},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2402.12001v1}
}

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