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Related Experiment Video

Updated: Jan 17, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Temporal knowledge graphs forecasting based on explainable temporal relation tree-graph.

Qihong Wu1, Ruizhe Ma2, Yuan Cheng3

  • 1College of Computer Science & Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China.

Neural Networks : the Official Journal of the International Neural Network Society
|January 14, 2026
PubMed
Summary

Temporal Relation Tree-based Learning (TRTL) models complex temporal dynamics in knowledge graphs. This interpretable AI approach enhances temporal forecasting by structuring multi-hop relation chains, outperforming existing methods.

Keywords:
Explainable link predictionKnowledge graphTemporal informationTree-LSTM

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Area of Science:

  • Artificial Intelligence
  • Knowledge Representation and Reasoning
  • Machine Learning

Background:

  • Real-world temporal knowledge graphs exhibit complex temporal dynamics.
  • Modeling multi-hop temporal relation chains and interpretable reasoning are key challenges in temporal knowledge graph forecasting.

Purpose of the Study:

  • To propose TRTL (Temporal Relation Tree-based Learning), a novel framework for temporal knowledge graph forecasting.
  • To address challenges in modeling complex temporal dynamics and enabling interpretable reasoning.

Main Methods:

  • Introduced two complementary graph structures: Sequence Grounding Graph and Temporal Relation Tree Graph.
  • Encoded graph structures using Tree-LSTM with attention mechanisms for temporal logic and dependency capture.
  • Employed a tree-based symbolic reasoning process for interpretable predictions.

Main Results:

  • TRTL effectively captures temporal logic and long-range dependencies.
  • The tree-based reasoning process enhances prediction transparency and reliability.
  • Experiments demonstrated TRTL significantly outperforms existing symbolic-based models on time-interval benchmarks.

Conclusions:

  • TRTL offers an effective and interpretable solution for temporal knowledge graph forecasting.
  • The proposed graph structures and Tree-LSTM encoding advance the state-of-the-art in temporal reasoning.
  • TRTL enhances the reliability and transparency of predictions in dynamic knowledge graphs.