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Eliminating Temporal Conflicts in Uncertain Temporal Knowledge Graphs [PDF]
, 2018 In 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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Relational learning on temporal knowledge graphs [PDF]
, 2022 Over the last decade, there has been an increasing interest in relational machine learning (RML), which studies methods for the statistical analysis of relational or graph-structured data. Relational data arise naturally in many real-world applications, including social networks, recommender systems, and computational finance.
Han, Zhen
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Representing temporal knowledge in conceptual graphs
Knowledge-Based Systems, 1991This study was motivated by some difficulties encountered by the authors when trying to express temporal knowledge using Sowa's conceptual graph (CG) approach. An overview of Sowa's approach is given and the difficulties encountered when trying to model temporal knowledge are outlined: the disparity of notations allowed by CG theory for expressing ...
Bernard Moulin, Daniel Côté
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Learning Dynamic Embeddings for Temporal Knowledge Graphs
Proceedings of the 14th ACM International Conference on Web Search and Data Mining, 2021Representation learning for temporal knowledge graphs has attracted increasing attention in recent years. In this paper, we study the problem of learning dynamic embeddings for temporal knowledge graphs. We address this problem by proposing a Dynamic Bayesian Knowledge Graphs Embedding model (DBKGE), which is able to dynamically track the semantic ...
Siyuan Liao +3 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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