A phenotype-driven and evidence-governed framework for knowledge graph enrichment and hypotheses discovery in population data

Confirmatory KG Construction: Current knowledge graph construction methods focus on recovering known relationships rather than identifying novel or context-dependent nodes.

Controlled KG Expansion: The work addresses the need for structured hypothesis discovery and controlled knowledge graph expansion.

Confirmatory KG Construction: Current knowledge graph construction methods focus on recovering known relationships rather than identifying novel or context-dependent nodes.

Controlled KG Expansion: The work addresses the need for structured hypothesis discovery and controlled knowledge graph expansion.

Phenotype-Driven Evidence-Governed KG Framework: The paper proposes a phenotype-driven and evidence-governed framework for structured hypothesis discovery and controlled KG expansion.

GNN Causal Probabilistic LLM Pipeline: The approach integrates GNNs, causal inference, probabilistic reasoning, and LLMs for phenotype discovery, hypothesis generation, and claim extraction in a unified pipeline.

Multi-Objective Pareto Claim Selection: KG expansion is formulated as a multi-objective optimization problem that evaluates candidate claims for relevance, structural validation, and novelty using Pareto-optimal selection.

Phenotype-Driven Evidence-Governed KG Framework: The paper proposes a phenotype-driven and evidence-governed framework for structured hypothesis discovery and controlled KG expansion.

GNN Causal Probabilistic LLM Pipeline: The approach integrates GNNs, causal inference, probabilistic reasoning, and LLMs for phenotype discovery, hypothesis generation, and claim extraction in a unified pipeline.

More Interpretable Phenotypes:

Context-Dependent Causal Structures Revealed:

High-Quality Evidence-Aligned Claims: