Applied Ontology

Mahdi Bu Ali

Mahdi Bu Ali completed his MSc in Computer Science at KAUST in 2025 under the supervision of Robert Hoehndorf. His research lay at the intersection of formal mathematics, automated theorem proving, and large language models. His thesis, Automated Theorem Proving with Large Language Models in Lean: An Exploration of Specialized In-Context Learning and General-Purpose Hierarchical Architectures, addresses the long-standing goal of automating mathematical reasoning in interactive theorem provers such as Lean. Automated theorem proving is constrained both by the cost of exploring vast proof search

Knowledge Representation and Reasoning (2015-2016)

Year: 2015-2016 Code: CS 213 Program: Computer Science Institution: KAUST Role: Instructor Topics: Ontology engineering, Applied Ontology

Applied Ontology (2016-2017)

Year: 2016-2017 Code: CS 390EE Program: Computer Science Institution: KAUST Role: Instructor Topics: Applied Ontology, Ontology engineering

Knowledge Representation and Reasoning (2017-2018)

Year: 2017-2018 Code: CS 213 Program: Computer Science Institution: KAUST Role: Instructor Topics: Ontology engineering, Applied Ontology

Applied Ontology (2018)

Year: 2018 Code: CS 322 Program: Computer Science Institution: KAUST Role: Instructor Topics: Applied Ontology, Ontology engineering

Knowledge Representation and Reasoning (2020)

Year: 2020 Code: CS 213 Program: Computer Science Institution: KAUST Role: Instructor Topics: Ontology engineering, Applied Ontology, Neuro-symbolic AI

Knowledge Representation and Reasoning (2021)

Year: 2021 Code: CS 213 Program: Computer Science Institution: KAUST Role: Instructor Topics: Ontology engineering, Applied Ontology, Neuro-symbolic AI

Knowledge Representation and Reasoning (2022)

Year: 2022 Code: CS 213 Program: Computer Science Institution: KAUST Role: Instructor Topics: Ontology engineering, Applied Ontology, Neuro-symbolic AI

Neurosymbolic AI (2024)

Year: 2024 Code: CS 394D Program: Computer Science Institution: KAUST Role: Instructor Topics: Neuro-symbolic AI, Applied Ontology

Knowledge Representation and Reasoning (2024)

Year: 2024 Code: CS 213 Program: Computer Science Institution: KAUST Role: Instructor Topics: Ontology engineering, Applied Ontology, Neuro-symbolic AI

Neurosymbolic AI (2026)

Year: 2026 Code: CS 394D Program: Computer Science Institution: KAUST Role: Instructor Topics: Neuro-symbolic AI, Applied Ontology

Knowledge Representation and Reasoning (2026)

Year: 2026 Code: CS 213 Program: Computer Science Institution: KAUST Role: Instructor Topics: Ontology engineering, Applied Ontology, Neuro-symbolic AI

Data integration and ontologies for microbial cell factories

Industrial biotechnology depends on integrating data from sources that were never designed to be combined: enzyme and reaction databases, protein-family resources, pathway maps, strain collections, phenotypic screens, and the experimental literature itself. Each uses different identifiers, different vocabularies, and different levels of granularity, and naive integration produces brittle pipelines that break whenever a source is updated. The Computational Bioscience Research Center's Microbial Cell Factories initiative, led by Vladimir Bajic with Hoehndorf as CoI heading the data-integration

Bio2Vec: Smart analytics infrastructure for the life sciences

By the mid-2010s the life sciences had produced an extraordinary investment in machine-readable knowledge: biomedical ontologies were used throughout biology to annotate data, and large RDF knowledge graphs such as Bio2RDF aggregated billions of statements from dozens of major databases. At the same time, large personal genomic datasets, the UK 100,000 Genomes project, UK Biobank, and the Saudi Human Genome Program, were coming online, and translating these into clinical insight depended on integrating them with that existing background knowledge. Generic knowledge-graph machine learning

Improvement of genetic variant prioritization technology

By 2018 the PhenomeNET Variant Predictor (PVP) had been validated as a research tool for prioritizing causative variants in Mendelian disease, and a clear question had emerged: could the same phenotype-driven, knowledge-graph-based machinery be retargeted to cancer, where the variant landscape is somatic, heterogeneous, and dominated by combinations of driver and passenger mutations rather than single pathogenic alleles? This one-year KAUST Center Partnership Fund project (2018–2019, OSR-2018-CPF-3657-0; with Schofield in Cambridge, Gkoutos in Birmingham, and Bajic at KAUST) was the

CompleX: Variant Prioritization in Complex Disease

The hardest cases in clinical genome sequencing are the ones where no single variant explains the disease. As Mendelian gene discovery slows and the diagnostic rate for whole-exome sequencing stalls below 50%, growing evidence points to oligogenic and polygenic origins: combinations of medium-rare or common alleles that, individually, look unremarkable. Population-level approaches lack the power to find them, and traditional single-gene Mendelian reasoning ignores them. The CompleX project (2019–2021, with the Universities of Cambridge and Birmingham) set out to break this impasse by extending

IBNSINA-QI: Integrating Biomedical Networks and Semantic Information for Neural network Analysis of Quantitative Information

Biological measurements are inherently high-dimensional and heterogeneous: omics platforms produce thousands to millions of features per individual, and they coexist with qualitative information such as diagnoses, phenotype calls, and prescriptions. Biomedical ontologies and knowledge graphs encode rich qualitative background knowledge, but they are largely disconnected from the quantitative measurements that gave rise to the categorical phenotypes in the first place. Conversely, graph neural networks and other methods that handle quantitative data on graphs do not yet exploit the formal

Computational methods for functional metagenomics: from protein functions to multi-scale interactions

Metagenomic sequencing has made it routine to read the DNA of an entire microbial community, but most analysis pipelines stop at taxonomic composition or at the level of individual protein families. The really biologically informative questions, which proteins do what, which proteins interact, which metabolic pathways are reconstructible, and how the community as a whole interacts with its environment or host, remain largely out of reach computationally. Even associations that are very robust empirically, for example between gut microbiome composition and colorectal cancer or inflammatory
crg explainable ml ontologies

Towards sound, complete, and explainable machine learning with biomedical ontologies (CRG11)

Deep learning has driven much of the recent progress in bioinformatics, but the resulting models are essentially black boxes: it is hard or impossible to ask why a prediction was made, to verify it against existing knowledge, or to detect biases that emerge from the interaction of dataset, architecture, and training objective. In clinical and biomedical settings these limitations matter, both because clinicians need to trust the systems they use and because formal guarantees of soundness and fairness can only be obtained when the inferential machinery can be inspected. The grand challenge