| Autor: |
Yuming SHEN, Jianfeng DU |
| Jazyk: |
čínština |
| Rok vydání: |
2021 |
| Předmět: |
|
| Zdroj: |
大数据, Vol 7, p 2021024 (2021) |
| Druh dokumentu: |
article |
| ISSN: |
2096-0271 |
| DOI: |
10.11959/j.issn.2096-0271.2021024 |
| Popis: |
Temporal knowledge graph (TKG) are obtained by adding the time information of real-world knowledge to classical knowledge graphs.Recently, TKG completion has drawn much attention and become a hot topic in research.Two main methodologies for TKG completion were summarized, one based on symbolic logic whereas and the other based on knowledge representation learning.The pros and cons of these two different methodologies were discussed, highlighting some directions for enhancing TKG completion in future research.Also, seven benchmark datasets for TKG completion and evaluation results of several typical models on the benchmark datasets were introduced. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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