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

Author:

Xiaxia Wang, Gong Cheng

Keyword:

Computer Science, Artificial Intelligence, Artificial Intelligence (cs.AI), Databases (cs.DB), Information Retrieval (cs.IR), Social and Information Networks (cs.SI)

journal:

date:

2024-02-19 00:00:00

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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