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It's About Time: Temporal Reasoning in Biomedical Knowledge Graphs Challenge

National Institutes of Health

Knowledge graphs (KGs) are foundational tools for biomedical research, clinical decision support, public health analytics, and AI-driven discovery. However, most existing biomedical and clinical KGs represent knowledge embedded as static factual information that lack the ability to encode how entities, relationships, and evidence may evolve over time. This limitation fundamentally hinders the accuracy, reliability, and trustworthiness of downstream analytics and AI systems that rely on knowledge in these graphs that may change over time.

In realworld settings, nearly all biomedical and clinical processes are temporal. Care pathways unfold over months to years as patients move through diagnostic, treatment, and recovery. Diagnostic criteria and clinical guidelines change as new evidence emerges. Treatments are often sequential and not interchangeable, and their appropriateness depends on disease stage, prior response, comorbidities, and evolving standards of care. Scientific claims may be supported, refined, or contradicted as new data accumulate. Public health risks shift across waves of outbreaks, variants, and available interventions. Without explicit temporal representation, KGs cannot distinguish “what is true now” from “what was once true,” nor can they reason over longitudinal trajectories or timerespecting causal paths to make future projections.

The absence of temporal embeddings and temporal reasoning mechanisms may lead to critical failures. Static KGs may conflate early and latestage therapies, allow temporally impossible inference paths, obscure exposure windows in contact tracing, merge outdated guidance with current recommendations, or treat contradictory scientific claims as equally valid. These shortcomings can result in incorrect reasoning, misinterpretation of evidence, and clinically implausible or unsafe recommendations. As AI agents increasingly query KGs to support trustworthy decisionmaking, these risks are amplified. Despite growing recognition of this challenge, most KGs and embedding methods still rely on static representations that are not designed to capture time. There is no widely adopted, interoperable approach for modeling temporal relationships (e.g., event sequencing, duration, validity intervals, evidence evolution, and uncertainty) in KGs.

This challenge seeks to address this gap by incentivizing the development of innovative methods, tools, or frameworks that enable robust temporal embeddings and temporal reasoning in KGs. Proposed solutions should allow downstream analytics and AI systems to accurately interpret data in temporal context, reason over sequences of events, and distinguish evolving evidence, guidelines, and interventions. Solutions should be applicable to realworld biomedical and clinical use cases and should align with and leverage existing standards where possible.

By advancing temporal KG capabilities, this Challenge aims to facilitate the development of new and novel methods, which unlock more accurate, contextaware, and trustworthy AIdriven insights that reflect how knowledge, evidence, and clinical workflows evolve over time.

This Challenge has two phases: 1) Concept Design and Feasibility, and 2) Prototype Implementation and Demonstration.

Phase 1 Deadline: Jan. 15, 2027

Specialties

Medical

Medical - Translational

Medical - Clinical Science

Eligibility Requirements

Sponsor Details

Sponsor Institute/Organizations: National Institutes of Health

Sponsor Type: Government/Federal

Address: National Institutes of Health; 31 Center Drive; MSC 2220; Bethesda; MD 20892-2220; USA

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