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A Survey on Temporal Knowledge Graph: Representation Learning and Applications
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Knowledge graphs have garnered significant research attention and are widely used to enhance downstream applications. However, most current studies mainly focus on static knowledge graphs, whose facts do not change with time, and disregard their dynamic evolution over time. As a result, temporal knowledge graphs have attracted more attention because a large amount of structured knowledge exists only within a specific period. Knowledge graph representation learning aims to learn low-dimensional vector embeddings for entities and relations in a knowledge graph. The representation learning of temporal knowledge graphs incorporates time information into the standard knowledge graph framework and can model the dynamics of entities and relations over time. In this paper, we conduct a comprehensive survey of temporal knowledge graph representation learning and its applications. We begin with an introduction to the definitions, datasets, and evaluation metrics for temporal knowledge graph representation learning. Next, we propose a taxonomy based on the core technologies of temporal knowledge graph representation learning methods, and provide an in-depth analysis of different methods in each category. Finally, we present various downstream applications related to the temporal knowledge graphs. In the end, we conclude the paper and have an outlook on the future research directions in this area.
Forward citations
Cited by 7 Pith papers
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THBKG: A Temporal Biomedical Knowledge Graph for Decision-Aligned Clinical Advancement Prediction
A timestamped biomedical knowledge graph lets clinical advancement be predicted from evidence available at decision time; graph propagation outperforms direct-evidence baselines at the top of the ranking.
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Worst-Case Optimal BGPs on Temporal Graphs
A linear-space index lets Leapfrog Triejoin evaluate arbitrary temporal basic graph patterns in worst-case-optimal time O(Q*·m·log N) under any variable ordering.
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Time Imprint: Learning Time-Aware Representations in Multi-Modal Knowledge Graphs
Treating time as an entity-level modality with median-K timestamp selection, attention pooling, and three-stage temporal injection yields large link-prediction gains on the hardest multi-modal ambiguity cases.
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Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods
A survey of n-ary knowledge representation learning methods proposes a two-dimensional taxonomy based on modeling technique and entity role/position awareness.
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Towards Foundation Model on Temporal Knowledge Graph Reasoning
POSTRA is a structure-only temporal knowledge graph model that transfers relation patterns and relative time orderings across datasets for zero-shot link prediction.
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Higher-order Structure Boosts Link Prediction on Temporal Graphs
HTGN adds hyperedge memory and hypergraph convolution to temporal GNNs, claiming better dynamic link prediction and lower memory cost, but the reported results are undermined by data inconsistencies and invalid proofs.
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Modeling the Diachronic Evolution of Legal Norms: An LRMoo-Based, Component-Level, Event-Centric Approach to Legal Knowledge Graphs
Proposes a component-level, event-centric LRMoo-based model for versioning legal norms, but provides no implementation to verify the claimed exact reconstruction.
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