A Web-Based Pipeline for Ontology-Free Biomedical Knowledge Graph Construction and Exploration

Abstract

The exponential growth of biomedical regulatory documentation has created an urgent need for automated, scalable knowledge structuring mechanisms that do not rely on rigid, expert-curated ontologies. This paper presents the system implementation and empirical analysis of the Ontology-Free Biomedical Knowledge Graph Induction (OF-Bio-KG) framework, focusing on three dimensions: (i) the end-to-end computational pipeline, (ii) the structural and statistical properties of the induced knowledge graph, and (iii) a proposed crowd-sourced expert evaluation strategy intended for future deployment. The OF-Bio-KG framework operates on a corpus of 1,068 European Medicines Agency (EMA) pharmaceutical product leaflets totalling over 131 million tokens. Using the nomic-embed-text model for long-context semantic encoding, a Balanced K-Means Constrained clustering algorithm for thematic organization, and LLaMA-3 8B for ontology-free relational inference, the system produces a multi-level knowledge graph comprising 256,148 leaf nodes, 3,742 cluster-level concept nodes, over 1.6 million intra-cluster edges, and 147,000 inter-cluster edges, all persisted in Neo4j. A central challenge in this work is the absence of a groundtruth biomedical knowledge graph against which the induced graph can be evaluated using standard metrics. To address this, we propose a human-in-the-loop validation strategy planned for future deployment: a purpose-built web application will be made available to Master's students at ENSIT, who will act as domain-proficient annotators to explore and validate graph components. This paper describes the current system, its empirical results, and the design of this forthcoming evaluation platform.