A Survey on Extractive Knowledge Graph Summarization: Applications, Approaches, Evaluation, and Future Directions
It addresses the need for compact KG representations to facilitate downstream tasks, but it is incremental as a survey rather than introducing new methods.
This survey paper tackles the problem of summarizing large Knowledge Graphs (KGs) by providing a systematic overview of extractive KG summarization, including its applications, methods, and future directions, based on an extensive and comparative review.
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.