Welcome to the website of the Bio-Ontology Research Group at KAUST. Our research focuses on the use of bio-ontologies for data integration and analysis in biology. We are interested in biological problems that require integration of multiple types of data and integration of data across scales and levels of granularity. We work on the development of biomedical ontologies, their application to data integration and annotation, and ontology-based neuro-symbolic machine learning models. Our areas of application include understanding the molecular basis of rare and common human diseases, personalizing diagnosis and treatment, prediction of functions and phenotypes, and biodiversity of plants and microbes. 

Latest News

New publication at ESWC 2026: Robust knowledge graph embedding via denoising

A paper at ESWC 2026 by Tengwei Song, Xudong Ma, Yang Liu, Jie Luo and Robert Hoehndorf introduces a unified framework that hardens knowledge graph embeddings against perturbations in the embedding space, with certified robustness guarantees.

New publication at ESWC 2026: SIDEKICK, a semantically integrated knowledge graph for drug safety

SIDEKICK, presented at ESWC 2026 by Mohammad Ashhad, Olga Mashkova, Ricardo Henao (Duke) and Robert Hoehndorf, builds an ontology-aligned knowledge graph of drug indications, contraindications and adverse reactions from 50,000+ FDA drug labels.

New publication at ESWC 2026: Fully geometric multi-hop reasoning on knowledge graphs with transitive relations

A new paper by Fernando Zhapa-Camacho and Robert Hoehndorf, presented at ESWC 2026, introduces GeometrE, a fully geometric box-embedding method that performs multi-hop reasoning on knowledge graphs without neural components for logical operations, and preserves transitive relations through an idempotency-enforcing loss.

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Research

BORG works on four connected strands: biomedical ontologies and semantic similarity; neuro-symbolic AI that grounds deep models in those ontologies; disease and phenotype informatics (rare-disease variant prioritization, drug mechanisms, population-scale genomics); and prediction of protein function, microbial communities and biodesign. See the Research page for topics, projects, and software.

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