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Temporal Knowledge Graph Completion: A Survey

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arxiv 2201.08236 v1 pith:DK6DJKD6 submitted 2022-01-16 cs.AI cs.LG

Borui Cai , Yong Xiang , Longxiang Gao , He Zhang , Yunfeng Li , Jianxin Li This is my paper
classification cs.AIcs.LG
keywords knowledgetkgcgraphmethodstemporalcompletionfactscapture
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Knowledge graph completion (KGC) can predict missing links and is crucial for real-world 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. Recently, emerging methods have shown improved predictive results by further incorporating the timestamps of facts; namely, temporal knowledge graph completion (TKGC). With this temporal information, TKGC methods can learn the dynamic evolution of the knowledge graph that KGC methods fail to capture. In this paper, for the first time, we summarize the recent advances in TKGC research. First, we detail the background of TKGC, including the problem definition, benchmark datasets, and evaluation metrics. Then, we summarize existing TKGC methods based on how timestamps of facts are used to capture the temporal dynamics. Finally, we conclude the paper and present future research directions of TKGC.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Worst-Case Optimal BGPs on Temporal Graphs

    cs.DB 2026-07 conditional novelty 7.0 of 10

    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.

  2. Hierarchical Graph Memory for LLM Agents with Path-level Localization and Rewrite

    cs.AI 2026-08 conditional novelty 6.0 of 10

    HiGram is a hierarchical graph memory with path-level localization and coordinated rewriting that improves long-term QA accuracy and token efficiency for LLM agents.

  3. KnowDR-REC: A Benchmark for Referring Expression Comprehension with Real-World Knowledge

    cs.LG 2025-08 conditional novelty 5.0 of 10

    KnowDR-REC is a benchmark that tests image-and-text AI models on object finding that needs real-world knowledge, and on 16 current models most of them fail.

  4. Rethinking Regularization Methods for Knowledge Graph Completion

    cs.LG 2025-05 reject novelty 4.0 of 10

    A selective sparsity regularizer, SPR, nudges link prediction metrics up on standard benchmarks, but the evidence is single-run and the theoretical justification is mathematically flawed.

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