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Eliminating Temporal Conflicts in Uncertain Temporal Knowledge Graphs
2018In the real world, a majority of facts are not static or immutable but highly ephemeral. Each fact is valid for only a limited amount of time, or it stands in temporal dependencies. In addition, facts with time information are usually accompanied by a real-valued weight which witnesses the possibility of a fact.
Lingjiao Lu +5 more
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Survey on Temporal Knowledge Graph
2021 IEEE Sixth International Conference on Data Science in Cyberspace (DSC), 2021Chong Mo +3 more
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Temporal knowledge graphs forecasting based on explainable temporal relation tree-graph
Neural NetworksIn real-world temporal knowledge graphs, relationships among entities often exhibit complex temporal dynamics. Effectively modeling multi-hop temporal relation chains and enabling interpretable reasoning remain core challenges in temporal knowledge graph forecasting, which we address with our proposed model, TRTL (Temporal Relation Tree-based Learning).
Qihong Wu +4 more
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Context-Aware Temporal Knowledge Graph Embedding
2019Knowledge graph embedding (KGE) is an important technique used for knowledge graph completion (KGC). However, knowledge in practice is time-variant and many relations are only valid for a certain period of time. This phenomenon highlights the importance of temporal knowledge graph embeddings.
Yu Liu 0053 +3 more
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Temporal Extrapolation and Knowledge Transfer for Lifelong Temporal Knowledge Graph Reasoning
Findings of the Association for Computational Linguistics: EMNLP 2023, 2023Zhongwu Chen +4 more
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Tensor factorization for temporal knowledge graph forecasting
NeurocomputingTensor factorization has long been a cornerstone of knowledge graph (KG) reasoning, achieving state-of-the-art performance on static link prediction tasks with models such as ComplEx. Despite their effectiveness and scalability in KG reasoning, these approaches have been largely overlooked for temporal knowledge graph (TKG) forecasting, i.e ...
Dileo, Manuel +3 more
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Multi-hop temporal knowledge graph reasoning with temporal path rules guidance
Expert Systems With Applications, 2023Luyi Bai, Lin Zhu, Xiangxi Meng
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KST-GCN: A Knowledge-Driven Spatial-Temporal Graph Convolutional Network for Traffic Forecasting
IEEE Transactions on Intelligent Transportation Systems, 2022Haifeng Li, Jiawei Zhu, Chao Tao
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Temporal enhanced inductive graph knowledge tracing
Applied Intelligence, 2023Donghee Han 0001 +4 more
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