Challenge: Existing frameworks for extrapolating knowledge graphs are incomplete and do not represent real-world knowledge.
Approach: They propose an explainable extrapolation reasoning framework that integrates propositional reasoning and first-order reasoning by introducing a reasoning graph that iteratively expands to find the answer.
Outcome: The proposed framework outperforms state-of-the-art baselines in explaining future facts based on past counterparts.

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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.
Approach: They propose a Temporal PAth-based reasoning model that is robust to ambiguous temporal data.
Outcome: The proposed model outperforms SOTA methods on the link prediction task.
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.
An Adaptive Logical Rule Embedding Model for Inductive Reasoning over Temporal Knowledge Graphs (2022.emnlp-main)

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Challenge: Existing methods for temporal knowledge graphs (TKGs) are incomplete and therefore lack interpretability.
Approach: They propose an interpretable temporal knowledge graph reasoning model that captures deep causal logic by learning rule embeddings.
Outcome: The proposed model outperforms state-of-the-art models on the ICEWS14, ICEW0515 and ICEw18 datasets.
HTCCN: Temporal Causal Convolutional Networks with Hawkes Process for Extrapolation Reasoning in Temporal Knowledge Graphs (2024.naacl-long)

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Challenge: Temporal knowledge graphs (TKGs) are powerful tools for storing and modeling dynamic facts.
Approach: They propose a Hawkes process-based temporal causal convolutional network for temporal reasoning under extrapolation settings.
Outcome: The proposed network is based on Hawkes process-based temporal causal convolutional network and captures the temporal evolution of facts.
SiMFy: A Simple Yet Effective Approach for Temporal Knowledge Graph Reasoning (2023.findings-emnlp)

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Challenge: Existing models for temporal knowledge graph reasoning suffer from low training efficiency and insufficient generalization ability.
Approach: They propose a temporal knowledge graph reasoning approach that uses multilayer perceptron to model the structural dependencies of events and adopts a fixed-frequency strategy to incorporate historical frequency during inference.
Outcome: The proposed model achieves state-of-the-art performance with faster convergence speed and better generalization ability.
STK-Adapter: Incorporating Evolving Graph and Event Chain for Temporal Knowledge Graph Extrapolation (2026.acl-long)

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Challenge: Temporal Knowledge Graphs (TKGs) store dynamic facts in the real world.
Approach: They propose a Spatial-Temporal Knowledge Adapter which integrates the evolving graph encoder and the LLM to facilitate TKG reasoning.
Outcome: The proposed method outperforms state-of-the-art methods on benchmark datasets and exhibits strong generalization capabilities in cross-dataset task.
Temporal Extrapolation and Knowledge Transfer for Lifelong Temporal Knowledge Graph Reasoning (2023.findings-emnlp)

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Challenge: Existing methods for lifelong TKG reasoning only address part of the challenges.
Approach: They propose a temporal-path-based reinforcement learning framework for lifelong TKG reasoning . they add temporal displacement into the action space of RL to extrapolate for the future .
Outcome: The proposed model outperforms existing models against well-adapted baselines on three lifelong TKG reasoning benchmarks.
Temporal Knowledge Graph Reasoning with Dynamic Hypergraph Embedding (2024.lrec-main)

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Challenge: Existing models that model temporal dynamics with knowledge graphs and graph convolution networks lack high-order interactions between objects in TKG, which is an important factor to predict future facts.
Approach: They propose to embed temporal knowledge graph reasoning by constructing hypergraphs based on temporal information graphs at different timestamps and then adapt dynamic meta-embedding to fit TKG.
Outcome: The proposed method outperforms baseline models on public TKG datasets and provides good interpretation for the predicted results.
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 .
Outcome: The proposed model oversimplifies and causes information loss on two datasets.
Temporal Fact Reasoning over Hyper-Relational Knowledge Graphs (2024.findings-emnlp)

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Challenge: Existing models of temporal fact reasoning do not explicitly specify temporal information for each fact.
Approach: They propose a new type of data structure called hyper-relational TKG to study temporal fact reasoning over HKGs.
Outcome: The proposed model is based on two new benchmark HTKG datasets . it provides additional key-value pairs (i.e., qualifiers) for each KG fact .

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