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

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

Xiaxia Wang, Gong Cheng

Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence
Survey Track. Pages 8290-8298. https://doi.org/10.24963/ijcai.2024/916

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.
Keywords:
Data Mining: DM: Knowledge graphs and knowledge base completion
Data Mining: DM: Mining graphs
Knowledge Representation and Reasoning: KRR: Semantic Web