BridgeMem: Causal Dyadic Transition Residuals for Temporal Knowledge Graph Forecasting

2026-09-24

AI Summary

This study addresses the accuracy limitations of existing temporal knowledge graph forecasting methods caused by neglecting entity-pair-specific transition evidence. To this end, we propose a Causal Binary Transition Residual Estimation framework that refines frozen model scores by explicitly modeling entity-pair-level transition patterns. The method introduces an empirical Bayes adaptive reader and an uncertainty gating mechanism, integrated with attention and log-likelihood ratio calibration techniques, to achieve robust performance across both sparse and dense historical data. Extensive experiments on five benchmark datasets demonstrate that our approach comprehensively outperforms nine baseline models, achieving a maximum improvement of 0.0216 in Mean Reciprocal Rank (MRR). These results validate the effectiveness of binary transition modeling for enhancing temporal knowledge graph forecasting.

Abstract

Temporal knowledge graph forecasting aims to infer future relational facts from the temporal structure of observed events. Existing forecasters mainly summarize history through entity states, relation states, paths, or exact recurrence. These views often miss pair-specific transition evidence, that is, the way prior relations between the query actor and a candidate change the odds of the target relation. We introduce BridgeMem, which estimates this quantity as a residual added to the log scores of a frozen full-vocabulary forecaster. For each candidate, BridgeMem retrieves the pair's events that strictly precede t, encodes their relations, directions, and lags, and converts them into a likelihood-ratio correction. A support-adaptive empirical-Bayes reader trusts exact transition counts where they are abundant and backs off to a learned attention estimator where they are sparse. The backbone's own uncertainty gates the correction, so confident queries and candidates without dyadic history are left unchanged. On five benchmarks, BridgeMem improves on the strongest of nine baselines from 2021--2026 in all 20 filtered MRR and Hits@{1,3,10} comparisons, with MRR gains of 0.0213, 0.0164, 0.0216, 0.0112, and 0.0028 over the best prior result. These results show the value of explicit dyadic transition modeling.

Problem

• Temporal Knowledge Graph Forecasting
• Dyadic Transition
• Pair-specific Evidence
• Relational Facts

Innovation

• Temporal Knowledge Graph Forecasting
• Dyadic Transition Residuals
• Empirical Bayes
• Support-Adaptive Attention
• Uncertainty Gating