Expert Knowledge & Machine Understanding: Bridging Reactome's Ontology with LLM Semantic Embeddings

Susanna Bravi; Riccardo De Luca; Rosa Sicilia; Christine Nardini; Mario Santoro

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arXiv · 2608.28178

Expert Knowledge & Machine Understanding: Bridging Reactome's Ontology with LLM Semantic Embeddings

Susanna Bravi·Riccardo De Luca·Rosa Sicilia·Christine Nardini·Mario Santoro

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Abstract

Biological knowledgebases like Reactome provide high-quality pathways that include biological elements' relationships and textual descriptions (metadata). The quality of such pathways is granted by manual curation, that presents, however, significant scalability challenges. Lately, numerous NLP tools have been proposed to cope with this issue, leveraging textual information to automatically expand biological knowledgebases. However, little exploration has been done so far to assess whether relationships among textual descriptions mirror higher order biological relationships. This study explores whether human-written descriptions in Reactome can be used to infer the experts' defined global hierarchical structure. To test this, we extracted from Reactome the Homo Sapiens hierarchy of pathways and their reactions (Reactome Hierarchy), and used textual metadata to reconstruct a Semantic Hierarchy, combining a sentence transformer model (SPECTER2) with a modified agglomerative nesting algorithm and a graph reconstruction algorithm. Quantitative (Laplacian Spectral Distance and Bootstrapping) and qualitative (global topological metrics) analyses confirm our hypothesis and indicate that the global hierarchical structure of pathways can be inferred by experts textual metadata.

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Susanna Bravi, Riccardo De Luca, Rosa Sicilia, Christine Nardini, Mario Santoro. 2026-08-28. Expert Knowledge & Machine Understanding: Bridging Reactome's Ontology with LLM Semantic Embeddings. https://arxiv.org/abs/2608.28178

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