A Survey on Extractive Knowledge Graph Summarization: Applications, Approaches, Evaluation, and Future Directions
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arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866929248095174656 |
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| author | Wang, Xiaxia Cheng, Gong |
| author_facet | Wang, Xiaxia Cheng, Gong |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_12001 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Survey on Extractive Knowledge Graph Summarization: Applications, Approaches, Evaluation, and Future Directions Wang, Xiaxia Cheng, Gong Artificial Intelligence Databases Information Retrieval Social and Information Networks 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. |
| title | A Survey on Extractive Knowledge Graph Summarization: Applications, Approaches, Evaluation, and Future Directions |
| topic | Artificial Intelligence Databases Information Retrieval Social and Information Networks |
| url | https://arxiv.org/abs/2402.12001 |