Results 1 to 10 of about 9,217,308 (160)

Learning temporal granularity with quadruplet networks for temporal knowledge graph completion [PDF]

Scientific Reports
Temporal Knowledge Graphs (TKGs) capture the dynamic nature of real-world facts by incorporating temporal dimensions that reflect their evolving states. These variations add complexity to the task of knowledge graph completion.
Rushan Geng, Cuicui Luo
doaj   +4 more sources

Geometry Interaction Embeddings for Interpolation Temporal Knowledge Graph Completion

Mathematics
Knowledge graphs (KGs) have become a cornerstone for structuring vast amounts of information, enabling sophisticated AI applications across domains. The progression to temporal knowledge graphs (TKGs) introduces time as an essential dimension, allowing ...
Xuechen Zhao, Jinfeng Miao, Fuqiang Yang, Shengnan Pang   +3 more
doaj   +4 more sources

Enhanced Temporal Knowledge Graph Completion via Learning High-Order Connectivity and Attribute Information

Applied Sciences, 2023
Temporal knowledge graph completion (TKGC) refers to the prediction and filling in of missing facts on time series, which is essential for many downstream applications.
Minwei Wen, Hongyan Mei, Wei Wang, Xing Zhang   +3 more
doaj   +4 more sources

Temporal Knowledge Graph Completion Using Box Embeddings

Proceedings of the AAAI Conference on Artificial Intelligence, 2022
Knowledge graph completion is the task of inferring missing facts based on existing data in a knowledge graph. Temporal knowledge graph completion (TKGC) is an extension of this task to temporal knowledge graphs, where each fact is additionally associated with a time stamp.
Johannes Messner, Ralph Abboud, Ismail Ilkan Ceylan   +2 more
core   +9 more sources

Temporal Knowledge Graph Completion: A Survey [PDF]

Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, 2023
Knowledge graph completion (KGC) predicts missing links and is crucial for real-life knowledge graphs, which widely suffer from incompleteness. KGC methods assume a knowledge graph is static, but that may lead to inaccurate prediction results because many facts in the knowledge graphs change over time.
Borui Cai, Yong Xiang 0001, Longxiang Gao, He Zhang 0034, Yunfeng Li, Jianxin Li 0001   +5 more
core   +4 more sources

Temporal knowledge graph reasoning using global and recent history information [PDF]

Scientific Reports
Since the static Knowledge Graph cannot meet the dynamics of knowledge in the real world, Temporal Knowledge Graph has become a potential method for processing temporal knowledge.
Changlong Wang, Jianlong Cao, Wenzheng Guo, Jie Hu, Yaoyao Hu, Yi Liu, Yawei Li, Huimei Tian, Pengjuan Lu, Wenhao Zhao, Zhixiong Yin   +10 more
doaj   +2 more sources

Integrating BERT-XL with multi-dimensional knowledge graphs for knowledge completion and relation reasoning in archival fragmented texts [PDF]

Scientific Reports
Archival fragmented texts pose considerable challenges for knowledge extraction owing to semantic deficiency, contextual discontinuity, and entity recognition ambiguity, all arising from document deterioration and incomplete digitization.
Zhenghan Li
doaj   +2 more sources

A Brief Survey on Deep Learning-Based Temporal Knowledge Graph Completion

Applied Sciences
Temporal knowledge graph completion (TKGC) is the task of inferring missing facts based on existing ones in a temporal knowledge graph. In recent years, various TKGC methods have emerged, among which deep learning-based methods have achieved state-of-the-
Ningning Jia, Cuiyou Yao
doaj   +3 more sources

Tucker decomposition-based temporal knowledge graph completion [PDF]

Knowledge-Based Systems, 2022
Knowledge graphs have been demonstrated to be an effective tool for numerous intelligent applications. However, a large amount of valuable knowledge still exists implicitly in the knowledge graphs. To enrich the existing knowledge graphs, recent years witness that many algorithms for link prediction and knowledge graphs embedding have been designed to ...
Pengpeng Shao, Guohua Yang, Dawei Zhang 0001, Jianhua Tao 0001, Feihu Che, Tong Liu   +5 more
openaire   +4 more sources

Context-aware Temporal Knowledge Graph Completion Based on Relation Constraints [PDF]

Jisuanji kexue, 2023
The existing temporal knowledge graph completion models only consider the structural information of the quadruple itself,ignoring the implicit neighbor information and the constraints of relationships on entities,which leads to the poor perfor-mance of ...
WANG Jingbin, LAI Xiaolian, LIN Xinyu, YANG Xinyi
doaj   +1 more source