Research & Development Analysis
Relational Multi-Path Enhancement for Extrapolative Relation Reasoning in Temporal Knowledge Graph
Authors: Linlin Zong, Chi Ma, Jiahui Zhou, Xinyue Liu, Wenxin Liang, Xianchao Zhang, Bo Xu
Publication: CIKM '25
Relation reasoning in temporal knowledge graph infers unknown or emerging relational dependencies from historical structured data. Traditional approaches face inherent limitations in capturing complex semantic correlations and structural patterns among relations. To tackle this problem, we propose the Relational Multi-path Enhancement network (RME), which primarily focuses on relation modeling to enrich relation representations through comprehensive multi-path analysis. RME consists of five key components: (1) Controlled random walk module creates multi-hop head-to-tail paths using an adaptive stopping rule that balances short- and long-term connections. (2) Shared path extraction module identifies both shared-head paths and shared-tail paths. (3) Time-decayed path encoding module processes these paths differently. (4) Gated information aggregation module combines path information to determine which parts matter most. (5) Attention decoding module makes the final prediction by focusing on the most relevant path features. Experiments on multiple TKG benchmark datasets demonstrate that RME outperforms the state-of-the-art methods in relation multi-path reasoning.
Executive Impact & Key Findings
RME significantly advances temporal knowledge graph reasoning, offering robust solutions for predicting evolving relational dependencies crucial for strategic decision-making in dynamic environments.
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MRR on WIKI Dataset
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Hits@1 on YAGO Dataset
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Outperformance over baselines
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Dynamic Relational Modeling
Deep Analysis & Enterprise Applications
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The Relational Multi-path Enhancement (RME) network addresses limitations in existing Temporal Knowledge Graph (TKG) reasoning by focusing explicitly on relation modeling and multi-path analysis. It introduces a novel framework to capture complex semantic correlations and structural patterns among relations over time, enhancing the accuracy of predicting unknown or emerging relational dependencies.
RME employs a controlled random walk to generate diverse multi-hop paths, distinguishing between shared-head and shared-tail patterns. It then uses time-decayed path encoding with exponential decay and Temporal Convolutional Networks (TCNs) to capture temporal relevance and structural features. A gated information aggregation module adaptively fuses these features, and an attention decoding module makes final predictions, prioritizing relevant path features.
Experiments across ICEWS14, YAGO, GDELT, and WIKI datasets show RME's superior performance in relation reasoning. It significantly outperforms state-of-the-art methods, particularly in MRR and Hits@1, validating its effectiveness in dynamic relational evolution and capturing complex relational trajectories.
Ablation studies confirm the critical role of RME's components. The gated relation graph information aggregation module effectively suppresses noisy paths, while the time-decayed path encoding, particularly the 'len-weight' strategy, optimizes for shorter, more reliable paths, enhancing precision in relational reasoning. These mechanisms are key to RME's robust performance.
RME Framework: Relational Multi-Path Processing
Controlled Random Walk
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Shared Path Extraction
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Time-Decayed Path Encoding
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Gated Info Aggregation
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Attention Decoding
| Strategy | MRR | Hits@1 | Hits@3 |
|---|---|---|---|
| main | 0.927 | 0.916 | 0.928 |
| mean | 0.928 | 0.915 | 0.916 |
| concat | 0.946 | 0.951 | 0.959 |
| gate (Ours) | 0.952 | 0.944 | 0.961 |
0.952
MRR with Gated Aggregation (YAGO)
Case Study: Diplomatic Relations Reasoning (ICEWS14)
Scenario: Query: (John Kerry, ?, Benjamin Netanyahu, t4). RME predicts the target relation 'Engage_in_negotiation' at t4.
Analysis: RME leverages multi-faceted diplomatic paths through its components: Controlled Random Walk (path exploration), time-decayed shared path extraction (prioritizing recent interactions), TCN/attention encoding, gated aggregation (fusing U.S./Israeli dynamics), and attention decoding. This demonstrates its capability to decode complex diplomatic interdependencies, crucial for international relations forecasting.
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