IJCAI 2024poster1 citations
A Survey on Extractive Knowledge Graph Summarization: Applications, Approaches, Evaluation, and Future 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.
Data Mining: DM: Knowledge graphs and knowledge base completionData Mining: DM: Mining graphsKnowledge Representation and Reasoning: KRR: Semantic Web
BibTeX
@inproceedings{ijcai2024p916,
title = {A Survey on Extractive Knowledge Graph Summarization: Applications, Approaches, Evaluation, and Future Directions},
author = {Wang, Xiaxia and Cheng, Gong},
booktitle = {Proceedings of the Thirty-Third International Joint Conference on
Artificial Intelligence, {IJCAI-24}},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
editor = {Kate Larson},
pages = {8290--8298},
year = {2024},
month = {8},
note = {Survey Track},
doi = {10.24963/ijcai.2024/916},
url = {https://doi.org/10.24963/ijcai.2024/916},
}