# xERTE: Explainable Reasoning on Temporal Knowledge Graphs for Forecasting Future Links

arXiv: https://arxiv.org/abs/2012.15537 · HTML: https://mlsanity.com/paper/2012.15537/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Dec 2020.

## Built on by (most cited first, 13 of 13)

- [Neural Bellman-Ford Networks: A General Graph Neural Network Framework for Link Prediction](https://mlsanity.com/paper/2106.06935/) (2021)
- [Temporal Knowledge Graph Forecasting with Neural ODE](https://mlsanity.com/paper/2101.05151/) (2021)
- [A Simple But Powerful Graph Encoder for Temporal Knowledge Graph Completion](https://mlsanity.com/paper/2112.07791/) (2021)
- [History Repeats: Overcoming Catastrophic Forgetting For Event-Centric Temporal Knowledge Graph Completion](https://mlsanity.com/paper/2305.18675/) (2023)
- [ONSEP: A Novel Online Neural-Symbolic Framework for Event Prediction Based on Large Language Model](https://mlsanity.com/paper/2408.07840/) (2024)
- [A Graph-Guided Reasoning Approach for Open-ended Commonsense Question Answering](https://mlsanity.com/paper/2303.10395/) (2023)
- [CEGRL-TKGR: A Causal Enhanced Graph Representation Learning Framework for Temporal Knowledge Graph Reasoning](https://mlsanity.com/paper/2408.07911/) (2024)
- [Toward Better Temporal Structures for Geopolitical Events Forecasting](https://mlsanity.com/paper/2601.00430/) (2026)
- [RLGNet: Repeating-Local-Global History Network for Temporal Knowledge Graph Reasoning](https://mlsanity.com/paper/2404.00586/) (2024)
- [VITA: Versatile Time Representation Learning for Temporal Hyper-Relational Knowledge Graphs](https://mlsanity.com/paper/2505.11803/) (2025)
- [Mixture Policy based Multi-Hop Reasoning over N-tuple Temporal Knowledge Graphs](https://mlsanity.com/paper/2505.12788/) (2025)
- [Towards Improving Long-Tail Entity Predictions in Temporal Knowledge Graphs through Global Similarity and Weighted Sampling](https://mlsanity.com/paper/2507.18977/) (2025)
- [Warranted Attention: Learning What to Pass from Attention to Prediction](https://mlsanity.com/paper/2606.30139/) (2026)

## Abstract (arXiv)

Modeling time-evolving knowledge graphs (KGs) has recently gained increasing interest. Here, graph representation learning has become the dominant paradigm for link prediction on temporal KGs. However, the embedding-based approaches largely operate in a black-box fashion, lacking the ability to interpret their predictions. This paper provides a link forecasting framework that reasons over query-relevant subgraphs of temporal KGs and jointly models the structural dependencies and the temporal dynamics. Especially, we propose a temporal relational attention mechanism and a novel reverse representation update scheme to guide the extraction of an enclosing subgraph around the query. The subgraph is expanded by an iterative sampling of temporal neighbors and by attention propagation. Our approach provides human-understandable evidence explaining the forecast. We evaluate our model on four benchmark temporal knowledge graphs for the link forecasting task. While being more explainable, our model obtains a relative improvement of up to 20% on Hits@1 compared to the previous best KG forecasting method. We also conduct a survey with 53 respondents, and the results show that the evidence extracted by the model for link forecasting is aligned with human understanding.