# CognTKE: A Cognitive Temporal Knowledge Extrapolation Framework

arXiv: https://arxiv.org/abs/2412.16557 · HTML: https://mlsanity.com/paper/2412.16557/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 37 references that are in the map.
- First posted on arXiv 21 Dec 2024.

## Built on by (most cited first, 6 of 6)

- [Negative-Aware Diffusion Process for Temporal Knowledge Graph Extrapolation](https://mlsanity.com/paper/2602.08815/) (2026)
- [Evolving Beyond Snapshots: Harmonizing Structure and Sequence via Entity State Tuning for Temporal Knowledge Graph Forecasting](https://mlsanity.com/paper/2602.12389/) (2026)
- [CountTRuCoLa: Rule Learning for Interpretable Temporal Knowledge Graph Forecasting](https://mlsanity.com/paper/2509.09474/) (2025)
- [Temporal Knowledge Graph Forecasting under Distribution Shifts: A Synthetic Evaluation](https://mlsanity.com/paper/2607.09232/) (2026)
- [Denoising the Future: Context-Aware Spectral Diffusion for Temporal Knowledge Graph Extrapolation](https://mlsanity.com/paper/2608.20804/) (2026)
- [A Temporal Knowledge Graph for Music Festival Lineup Forecasting](https://mlsanity.com/paper/2609.24467/) (2026)

## Abstract (arXiv)

Reasoning future unknowable facts on temporal knowledge graphs (TKGs) is a challenging task, holding significant academic and practical values for various fields. Existing studies exploring explainable reasoning concentrate on modeling comprehensible temporal paths relevant to the query. Yet, these path-based methods primarily focus on local temporal paths appearing in recent times, failing to capture the complex temporal paths in TKG and resulting in the loss of longer historical relations related to the query. Motivated by the Dual Process Theory in cognitive science, we propose a Cognitive Temporal Knowledge Extrapolation framework (CognTKE), which introduces a novel temporal cognitive relation directed graph (TCR-Digraph) and performs interpretable global shallow reasoning and local deep reasoning over the TCR-Digraph. Specifically, the proposed TCR-Digraph is constituted by retrieving significant local and global historical temporal relation paths associated with the query. In addition, CognTKE presents the global shallow reasoner and the local deep reasoner to perform global one-hop temporal relation reasoning (System 1) and local complex multi-hop path reasoning (System 2) over the TCR-Digraph, respectively. The experimental results on four benchmark datasets demonstrate that CognTKE achieves significant improvement in accuracy compared to the state-of-the-art baselines and delivers excellent zero-shot reasoning ability. textitThe code is available at https://github.com/WeiChen3690/CognTKE.