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

TL;DR

This survey comprehensively reviews extractive knowledge graph summarization, discussing its applications, methodologies, evaluation, and future research directions to aid in managing large KGs.

Contribution

It provides the first systematic overview and taxonomy of extractive KG summarization methods, highlighting interdisciplinary approaches and future research avenues.

Findings

Identifies key applications of KG summarization.

Classifies existing methods into a taxonomy.

Outlines future research directions.

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.