Survey on Few-Shot Knowledge Graph Completion Technology
Few-shot knowledge graph completion (FKGC) is a new research hotspot in the field of knowledge graph completion, which aims to complete knowledge graph with a few samples of data. This task is of great importance in practical application and the fields of knowledge graph. In order to further promote...
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| Format: | Article |
| Language: | zho |
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Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press
2023-06-01
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| Series: | Jisuanji kexue yu tansuo |
| Subjects: | |
| Online Access: | http://fcst.ceaj.org/fileup/1673-9418/PDF/2209069.pdf |
| _version_ | 1797809625371770880 |
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| author | PENG Yanfei, ZHANG Ruisi, WANG Ruihua, GUO Jialong |
| author_facet | PENG Yanfei, ZHANG Ruisi, WANG Ruihua, GUO Jialong |
| author_sort | PENG Yanfei, ZHANG Ruisi, WANG Ruihua, GUO Jialong |
| collection | DOAJ |
| description | Few-shot knowledge graph completion (FKGC) is a new research hotspot in the field of knowledge graph completion, which aims to complete knowledge graph with a few samples of data. This task is of great importance in practical application and the fields of knowledge graph. In order to further promote the development of the field of FKGC, this paper summarizes and analyzes the current methods. Firstly, this paper describes the concept of FKGC and related content. Secondly, three types of FKGC methods are summarized based on technical methods, including scale learning-based methods, meta learning-based methods, and other model-based methods. In addition, this paper analyzes and summarizes each method from the perspectives of model core, model ideas, advantages and disadvantages, etc. Then, the datasets and evaluation indexes of FKGC method are summarized, and the FKGC method is analyzed from two aspects of model characteristics and experimental results. Finally, starting from the practical problems, this paper summarizes the difficult problems of the current FKGC task, analyses the difficulties behind the problems, gives the corresponding solutions, and prospects several development directions that deserve attention in this field in the future. |
| first_indexed | 2024-03-13T06:55:29Z |
| format | Article |
| id | doaj.art-0d63d2ed20f04c478da3090a5e594163 |
| institution | Directory Open Access Journal |
| issn | 1673-9418 |
| language | zho |
| last_indexed | 2024-03-13T06:55:29Z |
| publishDate | 2023-06-01 |
| publisher | Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press |
| record_format | Article |
| series | Jisuanji kexue yu tansuo |
| spelling | doaj.art-0d63d2ed20f04c478da3090a5e5941632023-06-07T07:58:32ZzhoJournal of Computer Engineering and Applications Beijing Co., Ltd., Science PressJisuanji kexue yu tansuo1673-94182023-06-011761268128410.3778/j.issn.1673-9418.2209069Survey on Few-Shot Knowledge Graph Completion TechnologyPENG Yanfei, ZHANG Ruisi, WANG Ruihua, GUO Jialong0School of Electronics and Information Engineering, Liaoning Technical University, Huludao, Liaoning 125100, ChinaFew-shot knowledge graph completion (FKGC) is a new research hotspot in the field of knowledge graph completion, which aims to complete knowledge graph with a few samples of data. This task is of great importance in practical application and the fields of knowledge graph. In order to further promote the development of the field of FKGC, this paper summarizes and analyzes the current methods. Firstly, this paper describes the concept of FKGC and related content. Secondly, three types of FKGC methods are summarized based on technical methods, including scale learning-based methods, meta learning-based methods, and other model-based methods. In addition, this paper analyzes and summarizes each method from the perspectives of model core, model ideas, advantages and disadvantages, etc. Then, the datasets and evaluation indexes of FKGC method are summarized, and the FKGC method is analyzed from two aspects of model characteristics and experimental results. Finally, starting from the practical problems, this paper summarizes the difficult problems of the current FKGC task, analyses the difficulties behind the problems, gives the corresponding solutions, and prospects several development directions that deserve attention in this field in the future.http://fcst.ceaj.org/fileup/1673-9418/PDF/2209069.pdfknowledge graph; knowledge graph completion; few-shot learning; few-shot knowledge graph comp-letion (fkgc) |
| spellingShingle | PENG Yanfei, ZHANG Ruisi, WANG Ruihua, GUO Jialong Survey on Few-Shot Knowledge Graph Completion Technology Jisuanji kexue yu tansuo knowledge graph; knowledge graph completion; few-shot learning; few-shot knowledge graph comp-letion (fkgc) |
| title | Survey on Few-Shot Knowledge Graph Completion Technology |
| title_full | Survey on Few-Shot Knowledge Graph Completion Technology |
| title_fullStr | Survey on Few-Shot Knowledge Graph Completion Technology |
| title_full_unstemmed | Survey on Few-Shot Knowledge Graph Completion Technology |
| title_short | Survey on Few-Shot Knowledge Graph Completion Technology |
| title_sort | survey on few shot knowledge graph completion technology |
| topic | knowledge graph; knowledge graph completion; few-shot learning; few-shot knowledge graph comp-letion (fkgc) |
| url | http://fcst.ceaj.org/fileup/1673-9418/PDF/2209069.pdf |
| work_keys_str_mv | AT pengyanfeizhangruisiwangruihuaguojialong surveyonfewshotknowledgegraphcompletiontechnology |