Results 21 to 30 of about 69,930 (266)
Diachronic Embedding for Temporal Knowledge Graph Completion
Proceedings of the AAAI Conference on Artificial Intelligence, 2020 Knowledge graphs (KGs) typically contain temporal facts indicating relationships among entities at different times. Due to their incompleteness, several approaches have been proposed to infer new facts for a KG based on the existing ones–a problem known as KG completion.
Rishab Goel +3 more
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Few-shot temporal knowledge graph completion based on meta-optimization
Complex & Intelligent Systems, 2023 Knowledge Graphs (KGs) have become an increasingly important part of artificial intelligence, and KGs have been widely used in artificial intelligence fields such as intelligent answering questions and personalized recommendation.
Lin Zhu +3 more
doaj +1 more source
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
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Temporal knowledge graph completion:methods and progress
大数据, 2021 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 ...
Yuming SHEN, Jianfeng DU
doaj
Unsupervised Entity Alignment for Temporal Knowledge Graphs
CoRR, 2023 Entity alignment (EA) is a fundamental data integration task that identifies equivalent entities between different knowledge graphs (KGs). Temporal Knowledge graphs (TKGs) extend traditional knowledge graphs by introducing timestamps, which have received increasing attention.
Xiaoze Liu +4 more
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Extrapolation over temporal knowledge graph via hyperbolic embedding
CAAI Transactions on Intelligence Technology, 2023 Predicting potential facts in the future, Temporal Knowledge Graph (TKG) extrapolation remains challenging because of the deep dependence between the temporal association and semantic patterns of facts.
Yan Jia +7 more
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Spatio-Temporal Relevance Classification from Geographic Texts Using Deep Learning
ISPRS International Journal of Geo-Information, 2023 The growing proliferation of geographic information presents a substantial challenge to the traditional framework of a geographic information analysis and service.
Miao Tian +7 more
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STAGCN: Spatial–Temporal Attention Graph Convolution Network for Traffic Forecasting
Mathematics, 2022 Traffic forecasting plays an important role in intelligent transportation systems. However, the prediction task is highly challenging due to the mixture of global and local spatiotemporal dependencies involved in traffic data.
Yafeng Gu, Li Deng
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3DRTE: 3D Rotation Embedding in Temporal Knowledge Graph
IEEE Access, 2020 Temporal knowledge graph (TKG) embedding has received increasing attention in the academia. However, most existing methods are extensions of traditional translation models.
Jingbin Wang +4 more
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From mice to humans—divergent strategies for intestinal homeostasis and regeneration
FEBS Letters, EarlyView.Recent advances such as organoid genome editing, xenotransplantation, imaging, and whole‐genome sequencing have enabled direct studies of human intestinal stem cells (ISCs). These studies reveal species‐specific features, including slower ISC proliferation, distinct injury responses, slower somatic mutation accumulation in humans, and an inverse ...
Keiko Ishikawa +2 more
wiley +1 more source