Evidence Paths: Grounding LLM-Generated Hypotheses in Semantic Web Knowledge Graphs

Abstract

Scientific discovery depends on connecting claims scattered across a fragmented literature—yet researchers’ tools offer either creative breadth or rigorous traceability, rarely both. We present a framework that unites large language models with Semantic Web knowledge graphs, enabling hypothesis exploration grounded in explicit, navigable chains of evidence. Our approach models a hypothesis as a semantic relation between concepts and introduces evidence paths: sequences of semantic predications, each published as a nanopublication with provenance metadata. These paths transform knowledge graphs into hypertextual spaces where navigation corresponds to traversing semantically meaningful, source-anchored links across the literature. The framework operates through an iterative loop. This iterative back-and-forth keeps exploration anchored to structured evidence whilst exploiting the generative capacity of language models to surface novel connections. We illustrate feasibility through a biomedical case study, showing how evidence paths expose both supporting mechanisms and countervailing risks. The framework contributes to hypertext research by treating scientific knowledge as a linked, traversable structure—advancing a vision of the WWW as a substrate for machine-assisted reasoning.