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SABET-QA introduces an iterative framework for temporal knowledge graph question answering that enhances multi-hop reasoning through bidirectional entity-temporal scoring and contextualization, showing consistent improvements over baselines on benchmarks like CronQuestions and TimeQuestions.
This paper introduces THBKG, a temporal heterogeneous biomedical knowledge graph covering 110k entities and 11.1M edges with yearly evidence timestamps, designed to predict whether target-disease pairs entering Phase II trials advance to Phase III using only evidence available at the decision time. The graph-based approach outperforms direct-evidence baselines, especially for pairs lacking direct evidence, and the authors release it as a continually updated resource.
TeRoR introduces a novel temporal knowledge graph embedding method that decouples entity temporal evolution with independent rotation transformations and uses relational circular regions to model diverse relation mapping properties, achieving competitive performance on four datasets.
Synapse is an open-source, self-hosted synthetic hippocampus for AI agents, using a temporal knowledge graph (Graphiti + FalkorDB) with salience scoring and Ebbinghaus forgetting curve to manage memory decay and consolidation, integrated as a Hermes agent memory provider.
This paper proposes a strikingness-aware evaluation framework for Temporal Knowledge Graph Reasoning (TKGR) that weights events by rarity to better assess model reasoning, addressing overestimation from trivial repeated events.
MemoryOS is an open-source, self-hosted AI agent memory tool using a temporal knowledge graph, achieving 86.2% accuracy on LongMemEval-s with fast 78ms retrieval speeds.