Survey on Construction Method of Temporal Knowledge Graph
As a bridge connecting data, knowledge, and intelligence, knowledge graph has been widely applied in fields such as search assistance, intelligent recommendation, question-answering systems, and natural language processing. However, with the expansion of application scenarios, static knowledge graph...
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| Format: | Article |
| Language: | zho |
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Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press
2025-02-01
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| Series: | Jisuanji kexue yu tansuo |
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| Online Access: | http://fcst.ceaj.org/fileup/1673-9418/PDF/2406089.pdf |
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| author | LU Jiamin, ZHANG Jing, FENG Jun, AN Qi |
| author_facet | LU Jiamin, ZHANG Jing, FENG Jun, AN Qi |
| author_sort | LU Jiamin, ZHANG Jing, FENG Jun, AN Qi |
| collection | DOAJ |
| description | As a bridge connecting data, knowledge, and intelligence, knowledge graph has been widely applied in fields such as search assistance, intelligent recommendation, question-answering systems, and natural language processing. However, with the expansion of application scenarios, static knowledge graph has shown limitations in handling dynamic knowledge. The emergence of temporal knowledge graph addresses this shortcoming by integrating temporal information into the graph structure, enabling a more accurate representation of dynamic changes in knowledge. This paper provides a comprehensive study on the construction of temporal knowledge graph. It begins by introducing the concept of temporal knowledge graph and clarifying its value in handling dynamic knowledge. Then, it delves into the construction process of temporal knowledge graph, dividing the core process into three key stages: knowledge extraction, knowledge fusion, and knowledge computing. Subsequently, it thoroughly organizes each stage, and each stage is detailed with task definitions, research summaries, and the application of large language models. In the knowledge extraction stage, it focuses on named entity recognition, relation extraction, and time information extraction; in the fusion stage, it discusses entity alignment and entity linking; and in the computation stage, it focuses on knowledge reasoning. Finally, it explores the challenges faced at each stage and looks forward to future research directions. |
| format | Article |
| id | doaj-art-8c4267592ba44011a552ca428833b4ae |
| institution | OA Journals |
| issn | 1673-9418 |
| language | zho |
| publishDate | 2025-02-01 |
| publisher | Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press |
| record_format | Article |
| series | Jisuanji kexue yu tansuo |
| spelling | doaj-art-8c4267592ba44011a552ca428833b4ae2025-08-20T02:08:23ZzhoJournal of Computer Engineering and Applications Beijing Co., Ltd., Science PressJisuanji kexue yu tansuo1673-94182025-02-0119229531510.3778/j.issn.1673-9418.2406089Survey on Construction Method of Temporal Knowledge GraphLU Jiamin, ZHANG Jing, FENG Jun, AN Qi01. College of Computer Science and Software Engineering, Hohai University, Nanjing 211100, China 2. Key Laboratory of Water Big Data Technology of Ministry of Water Resources, Hohai University, Nanjing 211100, ChinaAs a bridge connecting data, knowledge, and intelligence, knowledge graph has been widely applied in fields such as search assistance, intelligent recommendation, question-answering systems, and natural language processing. However, with the expansion of application scenarios, static knowledge graph has shown limitations in handling dynamic knowledge. The emergence of temporal knowledge graph addresses this shortcoming by integrating temporal information into the graph structure, enabling a more accurate representation of dynamic changes in knowledge. This paper provides a comprehensive study on the construction of temporal knowledge graph. It begins by introducing the concept of temporal knowledge graph and clarifying its value in handling dynamic knowledge. Then, it delves into the construction process of temporal knowledge graph, dividing the core process into three key stages: knowledge extraction, knowledge fusion, and knowledge computing. Subsequently, it thoroughly organizes each stage, and each stage is detailed with task definitions, research summaries, and the application of large language models. In the knowledge extraction stage, it focuses on named entity recognition, relation extraction, and time information extraction; in the fusion stage, it discusses entity alignment and entity linking; and in the computation stage, it focuses on knowledge reasoning. Finally, it explores the challenges faced at each stage and looks forward to future research directions.http://fcst.ceaj.org/fileup/1673-9418/PDF/2406089.pdftemporal knowledge graph; knowledge extraction; temporal information extraction; knowledge fusion; knowledge reasoning |
| spellingShingle | LU Jiamin, ZHANG Jing, FENG Jun, AN Qi Survey on Construction Method of Temporal Knowledge Graph Jisuanji kexue yu tansuo temporal knowledge graph; knowledge extraction; temporal information extraction; knowledge fusion; knowledge reasoning |
| title | Survey on Construction Method of Temporal Knowledge Graph |
| title_full | Survey on Construction Method of Temporal Knowledge Graph |
| title_fullStr | Survey on Construction Method of Temporal Knowledge Graph |
| title_full_unstemmed | Survey on Construction Method of Temporal Knowledge Graph |
| title_short | Survey on Construction Method of Temporal Knowledge Graph |
| title_sort | survey on construction method of temporal knowledge graph |
| topic | temporal knowledge graph; knowledge extraction; temporal information extraction; knowledge fusion; knowledge reasoning |
| url | http://fcst.ceaj.org/fileup/1673-9418/PDF/2406089.pdf |
| work_keys_str_mv | AT lujiaminzhangjingfengjunanqi surveyonconstructionmethodoftemporalknowledgegraph |