Negative-Aware Diffusion Process for Temporal Knowledge Graph Extrapolation (2026.findings-eacl)
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| Challenge: | Temporal Knowledge Graphs (TKGs) are dynamic structures representing entities and their evolving relationships through time. |
| Approach: | They propose a non-parametric model that encodes subject-centric histories into sequential embeddings. |
| Outcome: | The proposed model encodes subject-centric histories of entities, relations and temporal intervals into sequential embeddings. |
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| Challenge: | Existing methods for Temporal Knowledge Graph reasoning capture indeterminacy in future events, but they are limited in capturing it. |
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DLTKG: Denoising Logic-based Temporal Knowledge Graph Reasoning (2025.findings-emnlp)
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| Challenge: | Current approaches to temporal knowledge representation face limited generalization to unseen facts and insufficient interpretability of reasoning processes. |
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| Challenge: | Existing models ignore ability to skip irrelevant snapshots according to entity-related relations in query . TKGC is difficult and even large-scale pre-trained language models such as gist ignore explicit temporal information. |
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A Unified Temporal Knowledge Graph Reasoning Model Towards Interpolation and Extrapolation (2024.acl-long)
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| Challenge: | Existing methods for temporal knowledge graphs de-emphasize temporal correlations between facts sequences and ignore inferring clues from missing facts. |
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RECIPE-TKG: From Sparse History to Structured Reasoning for LLM-based Temporal Knowledge Graph Completion (2026.eacl-long)
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| Challenge: | Temporal Knowledge Graphs (TKGs) represent dynamic facts as timestamped relations between entities. Large Language Models (LLMs) have sparked interest in using pretrained generative models for TKG completion. |
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| Challenge: | Existing text-based methods for Temporal Knowledge Graph Reasoning struggle to balance textual knowledge and temporal information with expensive purpose-built training strategies. |
| Approach: | They propose a Contrastive historical modeling framework with prefix-tuning for TEmporal Reasoning that feeds history-contextualized text into the pseudo-Siamese encoders to strike a textual-temporal balance. |
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Temporal Knowledge Graph Forecasting Without Knowledge Using In-Context Learning (2023.emnlp-main)
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| Challenge: | Temporal knowledge graphs (TKGs) are used to represent real-world facts in a structured way. |
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Temporal Knowledge Graph Reasoning Based on N-tuple Modeling (2023.findings-emnlp)
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| Challenge: | Existing Temporal Knowledge Graphs (TKGs) only contain their core entities and form them as quadruples. |
| Approach: | They propose to describe a temporal fact more accurately as an n-tuple . they propose to use a neural network to learn evolutional representations of entities . |
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GenTKG: Generative Forecasting on Temporal Knowledge Graph with Large Language Models (2024.findings-naacl)
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| Challenge: | Existing methods for temporal relational forecasting are limited and require limited training data. |
| Approach: | They propose a retrieval-augmented generation framework that uses temporal logical rule-based retrieval and parameter-efficient instruction tuning to solve temporal knowledge forecasting challenges. |
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Graph Hawkes Transformer for Extrapolated Reasoning on Temporal Knowledge Graphs (2022.emnlp-main)
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| Challenge: | Existing methods for entity prediction cannot predict when an event will occur . there are many facts not related to the query that can confuse the model . |
| Approach: | They propose a temporal knowledge Graph reasoning model based on Graph Hawkes Transformer . the model captures instantaneous structural and temporal evolution information . |
| Outcome: | The proposed model performs much better under long-term evolution scenarios. |