Applied Ontology

BioHackathon series in 2011 and 2012: penetration of ontology and linked data in life science domains.

Year: 2014 Venue: Journal of biomedical semantics Authors: Toshiaki Katayama, Mark D. Wilkinson, Kiyoko F. Aoki-Kinoshita, Shuichi Kawa\-shima, Yasunori Yamamoto, Atsuko Yamaguchi, Shinobu Okamoto, Shin Kawano, Jin-Dong Kim, Yue Wang, Hongyan Wu, Yoshinobu Kano, Hiromasa Ono, Hidemasa Bono, Simon Kocbek, Jan Aerts, Yukie Akune, Erick Antezana, Kazuharu Arakawa, Bruno Aranda, Joachim Baran, Jerven Bolleman, Raoul Jp Bonnal, Pier Luigi Buttigieg, Matthew P. Campbell, Yi-An Chen, Hirokazu Chiba, Peter Ja Cock, Kevin B. Cohen, Alexandru Constantin, Geraint Duck, Michel Dumontier, Takatomo Fujisawa

Datamining with Ontologies

Year: 2016 Venue: Data Mining Techniques for the Life Sciences Authors: Robert Hoehndorf, Georgios V. Gkoutos, Paul N. Schofield DOI: 10.1007/978-1-4939-3572-7_19 Abstract The use of ontologies has increased rapidly over the past decade and they now provide a key component of most major databases in biology and biomedicine. Consequently, datamining over these databases benefits from considering the specific structure and content of ontologies, and several methods have been developed to use ontologies in datamining applications. Here, we discuss the principles of ontology structure, and

JOWO 2020: The Joint Ontology Workshops : Proceedings of the Joint Ontology Workshops co-located with the Bolzano Summer of Knowledge (BOSK 2020)

Year: 2020 Venue: CEUR-WS Topics Applied Ontology · Ontology engineering

Fully Geometric Multi-hop Reasoning on Knowledge Graphs with Transitive Relations

Year: 2026 Venue: The Semantic Web -- ESWC 2026 Authors: Fernando Zhapa-Camacho, Robert Hoehndorf DOI: 10.1007/978-3-032-25156-5_14 Abstract Multi-hop logical reasoning on knowledge graphs requires faithfully mapping the logical semantics to latent space. Current geometric embedding methods show to be useful on this task by mapping entities to geometric regions and logical operations to latent transformations. While a geometric embedding can provide a direct interpretability framework for query answering, current methods have only leveraged the geometric construction of entities, failing to

Using SPARQL to Unify Queries over Data, Ontologies, and Machine Learning Models in the PhenomeBrowser Knowledgebase

Year: 2022 Venue: Proceedings of the 13th International Conference on Semantic Web Applications and Tools for Health Care and Life Sciences, SWAT4HCLS 2022 Authors: Ali Syed, Senay Kafkas, Maxat Kulmanov, Robert Hoehndorf Topics Ontology engineering · Applied Ontology Acknowledged projects ccf-microbial-cell-factories crg-complex-variant-prioritization crg-ibnsina-qi

Experiences with Aber-OWL, an Ontology Repository with OWL EL Reasoning

Year: 2016 Venue: Ontology Engineering: 12th International Experiences and Directions Workshop on OWL, OWLED 2015, co-located with ISWC 2015, Bethlehem, PA, USA, October 9-10, 2015, Revised Selected Papers Authors: Luke Slater, Miguel Rodriguez-Garcia, Keiron O'Shea, Paul N. Schofield, Georgios V. Gkoutos, Robert Hoehndorf DOI: 10.1007/978-3-319-33245-1_8 Topics Applied Ontology · Ontology engineering

Ontology based mining of pathogen-disease associations from literature

Year: 2018 Venue: Bio-Ontologies COSI Authors: Senay Kafkas, Robert Hoehndorf Topics Biomedical informatics · Applied Ontology

To MIREOT or not to MIREOT? A case study of the impact of using MIREOT in the Experimental Factor Ontology (EFO)

Year: 2016 Venue: International Conference on Biomedical Ontology and BioCreative (ICBO BioCreative 2016) Authors: Luke Slater, Georgios V. Gkoutos, Paul N Schofield, Robert Hoehndorf Abstract MIREOT is a mechanism for the selective re-use of individual ontology classes in other ontologies. Designed to minimise effort and to support orthogonality, it is now in widespread use. The consequences for ontology integrity and automated reasoning of using the MIREOT mechanism have so far not been fully assessed. In this paper, we perform an analysis of the Experimental Factor Ontology (EFO), an

Large-Scale Reasoning over Functions in Biomedical Ontologies

Year: 2016 Venue: Formal Ontology in Information Systems Authors: Robert Hoehndorf, Liam Mencel, Georgios V. Gkoutos, Paul N. Schofield Abstract A large number of biomedical resources have been developed to represent the functions of biological entities, and these resources are widely used for data integration and analysis. Expressing functions in biomedical ontologies currently uses formal representation patterns that renders basic reasoning tasks to fall in complexity classes beyond polynomial time, thereby limiting the potential of using knowledge-based methods for data integration

Updating the CEMO ontology for future epidemiological challenges

Year: 2023 Venue: 14th International Conference on Semantic Web Applications and Tools for Health Care and Life Sciences (SWAT4HCLS 2023), Basel, Switzerland, February 13-16, 2023 Authors: N\'uria Queralt-Rosinach, Paul N. Schofield, Marco Roos, Robert Hoehndorf Abstract The COVID-19 epidemiology and monitoring ontology (CEMO) is an OWL ontology built during the COVID-19 pandemic for better exchange, integration and reuse of epidemiological information. Here, we present an update of the development of the ontology and future directions in order to make it usable under different scenarios and

Exploring the Use of Ontology Components for Distantly-Supervised Disease and Phenotype Named Entity Recognition

Year: 2023 Venue: Proceedings of the International Conference on Biomedical Ontologies 2023 together with the Workshop on Ontologies for Infectious and Immune-Mediated Disease Data Science (OIIDDS 2023) and the FAIR Ontology Harmonization and TRUST Data Interoperability Workshop (FOHTI 2023), Bras\', Brazil, August 28 - September 1, 2023 Authors: Sumyyah Toonsi, Senay Kafkas, Robert Hoehndorf Abstract The lack of curated corpora is one of the major obstacles for Named Entity Recognition (NER). With the advancements in deep learning and development of robust language models, distant supervision

Enriched biodiversity data as a resource and service

Year: 2014 Venue: Biodiversity Data Journal Authors: Rutger Vos, Jordan Biserkov, Bachir Balech, Niall Beard, Matthew Blissett, Christian Brenninkmeijer, Tom van Dooren, David Eades, George Gosline, Quentin Groom, Thomas Hamann, Hannes Hettling, Robert Hoehndorf, Ayco Holleman, Peter Hovenkamp, Patricia Kelbert, David King, Don Kirkup, Youri Lammers, Thibaut DeMeulemeester, Daniel Mietchen, Jeremy Miller, Ross Mounce, Nicola Nicolson, Rod Page, Aleksandra Pawlik, Serrano Pereira, Lyubomir Penev, Kevin Richards, Guido Sautter, David Shorthouse, Marko Tahtinen, Claus Weiland, Alan Williams

Analyzing gene expression data in mice with the Neuro Behavior Ontology

Year: 2014 Venue: Mamm Genome Authors: R. Hoehndorf, J. M. Hancock, N. W. Hardy, A. M. Mallon, P. N. Schofield, G. V. Gkoutos Abstract We have applied the Neuro Behavior Ontology (NBO), an ontology for the annotation of behavioral gene functions and behavioral phenotypes, to the annotation of more than 1,000 genes in the mouse that are known to play a role in behavior. These annotations can be explored by researchers interested in genes involved in particular behaviors and used computationally to provide insights into the behavioral phenotypes resulting from differences in gene expression. We

Thematic series on biomedical ontologies in JBMS: challenges and new directions

Year: 2014 Venue: Journal of Biomedical Semantics Authors: Robert Hoehndorf, Melissa Haendel, Robert Stevens, Dietrich Rebholz-Schuhmann Abstract Over the past 15 years, the biomedical research community has increased its efforts to produce ontologies encoding biomedical knowledge, and to provide the corresponding infrastructure to maintain them. As ontologies are becoming a central part of biological and biomedical research, a communication channel to publish frequent updates and latest developments on them would be an advantage.Here, we introduce the JBMS thematic series on Biomedical

The Semanticscience Integrated Ontology (SIO) for biomedical research and knowledge discovery

Year: 2014 Venue: Journal of Biomedical Semantics Authors: Michel Dumontier, Christopher Baker, Joachim Baran, Alison Callahan, Leonid Chepelev, Jose Cruz-Toledo, Nicholas Del Rio, Geraint Duck, Laura Furlong, Nichealla Keath, Dana Klassen, James McCusker, Nuria Queralt-Rosinach, Matthias Samwald, Natalia Villanueva-Rosales, Mark Wilkinson, Robert Hoehndorf Abstract The Semanticscience Integrated Ontology (SIO) is an ontology to facilitate biomedical knowledge discovery. SIO features a simple upper level comprised of essential types and relations for the rich description of arbitrary (real

Semantic units: organizing knowledge graphs into semantically meaningful units of representation

Year: 2024 Venue: Journal of Biomedical Semantics Authors: Lars Vogt, Tobias Kuhn, Robert Hoehndorf DOI: 10.1186/s13326-024-00310-5 Abstract Abstract Background In today’s landscape of data management, the importance of knowledge graphs and ontologies is escalating as critical mechanisms aligned with the FAIR Guiding Principles—ensuring data and metadata are Findable, Accessible, Interoperable, and Reusable. We discuss three challenges that may hinder the effective exploitation of the full potential of FAIR knowledge graphs. Results We introduce “semantic units” as a conceptual solution

Effects of Negation and Uncertainty Stratification on Text-Derived Patient Profile Similarity

Year: 2021 Venue: Frontiers in Digital Health Authors: Luke T. Slater, Andreas Karwath, Robert Hoehndorf, Georgios V. Gkoutos DOI: 10.3389/fdgth.2021.781227 Topics Applied Ontology · Semantic similarity

Improved characterisation of clinical text through ontology-based vocabulary expansion

Year: 2021 Venue: Journal of Biomedical Semantics Authors: Luke T. Slater, William Bradlow, Simon Ball, Robert Hoehndorf, Georgios V Gkoutos DOI: 10.1186/s13326-021-00241-5 Topics Applied Ontology Acknowledged projects crg-complex-variant-prioritization

Towards semantic interoperability: finding and repairing hidden contradictions in biomedical ontologies

Year: 2020 Venue: BMC Medical Informatics and Decision Making Authors: Luke T. Slater, Georgios V. Gkoutos, Robert Hoehndorf DOI: 10.1186/s12911-020-01336-2 Abstract Abstract Background Ontologies are widely used throughout the biomedical domain. These ontologies formally represent the classes and relations assumed to exist within a domain. As scientific domains are deeply interlinked, so too are their representations. While individual ontologies can be tested for consistency and coherency using automated reasoning methods, systematically combining ontologies of multiple domains together may

Ontology-based validation and identification of regulatory phenotypes

Year: 2018 Venue: Bioinformatics Authors: Maxat Kulmanov, Paul N Schofield, Georgios V Gkoutos, Robert Hoehndorf Abstract Motivation: Function annotations of gene products, and phenotype annotations of genotypes, provide valuable information about molecular mechanisms that can be utilized by computational methods to identify functional and phenotypic relatedness, improve our understanding of disease and pathobiology, and lead to discovery of drug targets. Identifying functions and phenotypes commonly requires experiments which are time-consuming and expensive to carry out; creating the

The anatomy of phenotype ontologies: principles, properties and applications

Year: 2018 Venue: Briefings in Bioinformatics Authors: Georgios V. Gkoutos, Paul N. Schofield, Robert Hoehndorf DOI: https://doi.org/10.1093/bib/bbx035 Abstract The past decade has seen an explosion in the collection of genotype data in domains as diverse as medicine, ecology, livestock and plant breeding. Along with this comes the challenge of dealing with the related phenotype data, which is not only large but also highly multidimensional. Computational analysis of phenotypes has therefore become critical for our ability to understand the biological meaning of genomic data in the biological

Ontology based mining of pathogen--disease associations from literature

Year: 2019 Venue: Journal of Biomedical Semantics Authors: Senay Kafkas, Robert Hoehndorf Topics Biomedical informatics · Applied Ontology Acknowledged projects ccf-microbial-cell-factories crg-bio2vec

Onto2Vec: joint vector-based representation of biological entities and their ontology-based annotations

Year: 2018 Venue: Bioinformatics Authors: Fatima Zohra Smaili, Xin Gao, Robert Hoehndorf Abstract Motivation: Biological knowledge is widely represented in the form of ontology-based annotations: ontologies describe the phenomena assumed to exist within a domain, and the annotations associate a (kind of) biological entity with a set of phenomena within the domain. The structure and information contained in ontologies and their annotations make them valuable for developing machine learning, data analysis and knowledge extraction algorithms; notably, semantic similarity is widely used to

An ontology approach to comparative phenomics in plants

Year: 2015 Venue: Plant Methods Authors: Anika Oellrich, Ramona Walls, Ethalinda Cannon, Steven Cannon, Laurel Cooper, Jack Gardiner, Georgios Gkoutos, Lisa Harper, Mingze He, Robert Hoehndorf, Pankaj Jaiswal, Scott Kalberer, John Lloyd, David Meinke, Naama Menda, Laura Moore, Rex Nelson, Anuradha Pujar, Carolyn Lawrence, Eva Huala DOI: 10.1186/s13007-015-0053-y Abstract BACKGROUND:Plant phenotype datasets include many different types of data, formats, and terms from specialized vocabularies. Because these datasets were designed for different audiences, they frequently contain language and

DELE: Deductive EL++ Embeddings for Knowledge Base Completion

Year: 2026 Venue: Neurosymbolic Artificial Intelligence Authors: Olga Mashkova, Fernando Zhapa-Camacho, Robert Hoehndorf DOI: 10.1177/29498732261420011 Abstract Ontology embeddings map classes, roles, and individuals in ontologies into R^n , and within R^n similarity between entities can be computed or new axioms inferred. For ontologies in the Description Logic EL^++ , several optimization-based embedding methods have been developed that explicitly generate models of an ontology. However, these methods suffer from some limitations; they do not distinguish between statements that are

Evaluating the effect of annotation size on measures of semantic similarity

Year: 2016 Venue: Proceedings of Bio-Ontologies SIG Authors: Maxat Kulmanov, Robert Hoehndorf Abstract Ontologies are widely used as metadata in biological and biomedical datasets. Measures of semantic similarity utilize ontologies to determine how similar two entities annotated with classes from ontologies are, and semantic similarity is increasingly applied in applications ranging from diagnosis of disease to investigation in gene networks and functions of gene products. Here, we analyze a large number of semantic similarity measures and the sensitivity of similarity values to the number of

The flora phenotype ontology (FLOPO): tool for integrating morphological traits and phenotypes of vascular plants

Year: 2016 Venue: Journal of Biomedical Semantics Authors: Robert Hoehndorf, Mona Alshahrani, Georgios V. Gkoutos, George Gosline, Quentin Groom, Thomas Hamann, Jens Kattge, Sylvia Mota de Oliveira, Marco Schmidt, Soraya Sierra, Erik Smets, Rutger A. Vos, Claus Weiland DOI: 10.1186/s13326-016-0107-8 Abstract The systematic analysis of a large number of comparable plant trait data can support investigations into phylogenetics and ecological adaptation, with broad applications in evolutionary biology, agriculture, conservation, and the functioning of ecosystems. Floras, i.e., books collecting

The role of ontologies in biological and biomedical research: a functional perspective

Year: 2015 Venue: Briefings in Bioinformatics Authors: Robert Hoehndorf, Paul N. Schofield, Georgios V. Gkoutos Abstract Ontologies are widely used in biological and biomedical research. Their success lies in their combination of four main features present in almost all ontologies: provision of standard identifiers for classes and relations that represent the phenomena within a domain; provision of a vocabulary for a domain; provision of metadata that describes the intended meaning of the classes and relations in ontologies; and the provision of machine-readable axioms and definitions that

A Review of Current Standards and the Evolution of Histopathology Nomenclature for Laboratory Animals

Year: 2018 Venue: ILAR Journal Authors: Charlotte M Keenan, Colin McKerlie, Georgios V Gkoutos, Jerrold M Ward, John P Sundberg, Mark F Cesta, Paul N Schofield, Robert Cardiff, Robert Hoehndorf, Susan A Elmore Abstract The need for international collaboration in rodent pathology has evolved since the 1970s and was initially driven by the new field of toxicologic pathology. First initiated by the World Health Organization’s International Agency for Research on Cancer for rodents, it has evolved to include pathology of the major species (rats, mice, guinea pigs, nonhuman primates, pigs, dogs

Notions of similarity for systems biology models

Year: 2018 Venue: Briefings in Bioinformatics Authors: Ron Henkel, Robert Hoehndorf, Tim Kacprowski, Christian Knupfer, Wolfgang Liebermeister, Dagmar Waltemath Abstract Systems biology models are rapidly increasing in complexity, size and numbers. When building large models, researchers rely on software tools for the retrieval, comparison, combination and merging of models, as well as for version control. These tools need to be able to quantify the differences and similarities between computational models. However, depending on the specific application, the notion of 'similarity' may greatly

A comprehensive update on CIDO: the community-based coronavirus infectious disease ontology

Year: 2022 Venue: Journal of Biomedical Semantics Authors: Yongqun He, Hong Yu, Anthony Huffman, Asiyah Yu Lin, Darren A. Natale, John Beverley, Ling Zheng, Yehoshua Perl, Zhigang Wang, Yingtong Liu, Edison Ong, Yang Wang, Philip Huang, Long Tran, Jinyang Du, Zalan Shah, Easheta Shah, Roshan Desai, Hsin-hui Huang, Yujia Tian, Eric Merrell, William D. Duncan, Sivaram Arabandi, Lynn M. Schriml, Jie Zheng, Anna Maria Masci, Liwei Wang, Hongfang Liu, Fatima Zohra Smaili, Robert Hoehndorf, Zoe May Pendlington, Paola Roncaglia, Xianwei Ye, Jiangan Xie, Yi-Wei Tang, Xiaolin Yang, Suyuan Peng, Luxia

FoodOn: a harmonized food ontology to increase global food traceability, quality control and data integration

Year: 2018 Venue: Science of Food Authors: Damion M. Dooley, Emma J. Griffiths, Gurinder S. Gosal, Pier L. Buttigieg, Robert Hoehndorf, Matthew C. Lange, Lynn M. Schriml, Fiona S. L. Brinkman, William W. L. Hsiao Topics Applied Ontology · Ontology engineering

DermO; an ontology for the description of dermatologic disease

Year: 2016 Venue: Journal of Biomedical Semantics Authors: Hannah M. Fisher, Robert Hoehndorf, Bruno S. Bazelato, Soheil S. Dadras, Lloyd E. King, Georgios V. Gkoutos, John P. Sundberg, Paul N. Schofield DOI: 10.1186/s13326-016-0085-x Abstract There have been repeated initiatives to produce standard nosologies and terminologies for cutaneous disease, some dedicated to the domain and some part of bigger terminologies such as ICD-10. Recently, formally structured terminologies, ontologies, have been widely developed in many areas of biomedical research. Primarily, these address the aim of

DES-TOMATO: A Knowledge Exploration System Focused On Tomato Species

Year: 2017 Venue: Scientific Reports Authors: Adil Salhi, Sonia Negrao, Magbubah Essack, Mitchell J. L. Morton, Salim Bougouffa, Rozaimi Mohamad Razali, Aleksandar Radovanovic, Benoit Marchand, Maxat Kulmanov, Robert Hoehndorf, Mark A. Tester, Vladimir B. Bajic Abstract Tomato is the most economically important horticultural crop used as a model to study plant biology and particularly fruit development. Knowledge obtained from tomato research initiated improvements in tomato and, being transferrable to other such economically important crops, has led to a surge of tomato-related research and

FALDO: a semantic standard for describing the location of nucleotide and protein feature annotation

Year: 2016 Venue: Journal of Biomedical Semantics Authors: Jerven T. Bolleman, Christopher J. Mungall, Francesco Strozzi, Joachim Baran, Michel Dumontier, Raoul P. J. Bonnal, Robert Buels, Robert Hoehndorf, Takatomo Fujisawa, Toshiaki Katayama, Peter A. J. Cock DOI: 10.1186/s13326-016-0067-z Abstract Nucleotide and protein sequence feature annotations are essential to understand biology on the genomic, transcriptomic, and proteomic level. Using Semantic Web technologies to query biological annotations, there was no standard that described this potentially complex location information as

Best behaviour? Ontologies and the formal description of animal behaviour

Year: 2015 Venue: Mammalian Genome Authors: Georgios V Gkoutos, Robert Hoehndorf, Loukia Tsaprouni, Paul N Schofield DOI: 10.1007/s00335-015-9590-y Abstract The development of ontologies for describing animal behavior has proved to be one of the most difficult of all scientific knowledge domains. Ranging from neurological processes to human emotions the range and scope needed for such ontologies is highly challenging, but if data integration and computational tools such as automated reasoning are to be fully applied in this important area the underlying principles of these ontologies need to

GFVO: the Genomic Feature and Variation Ontology

Year: 2015 Venue: PeerJ Authors: Joachim Baran, Bibi Sehnaaz Begum Durgahee, Karen Eilbeck, Erick Antezana, Robert Hoehndorf, Michel Dumontier DOI: 10.7717/peerj.933 Abstract Falling costs in genomic laboratory experiments have led to a steady increase of genomic feature and variation data. Multiple genomic data formats exist for sharing these data, and whilst they are similar, they are addressing slightly different data viewpoints and are consequently not fully compatible with each other. The fragmentation of data format specifications makes it hard to integrate and interpret data for further

Ontology-based prediction of cancer driver genes

Year: 2019 Venue: Scientific Reports Authors: Sara Althubaiti, Andreas Karwath, Ashraf Dallol, Adeeb Noor, Shadi Salem Alkhayyat, Rolina Alwassia, Katsuhiko Mineta, Takashi Gojobori, Andrew D Beggs, Paul N Schofield, Georgios V Gkoutos, Robert Hoehndorf Abstract Identifying and distinguishing cancer driver genes among thousands of candidate mutations remains a major challenge. Accurate identification of driver genes and driver mutations is critical for advancing cancer research and personalizing treatment based on accurate stratification of patients. Due to inter-tumor genetic heterogeneity

Improving the classification of cardinality phenotypes using collections

Year: 2023 Venue: Journal of Biomedical Semantics Authors: Sarah M. Alghamdi, Robert Hoehndorf DOI: 10.1186/s13326-023-00290-y Abstract We reformulate the phenotypes of collections of entities using an ontological theory of collections. By reformulating phenotypes of collections in phenotypes ontologies, we avoid potentially incorrect inferences pertaining to the cardinality of these collections. We apply our method to two phenotype ontologies and show that the reformulation not only removes some problematic inferences but also quantitatively improves biological data analysis. Topics Applied

Quantitative evaluation of ontology design patterns for combining pathology and anatomy ontologies

Year: 2019 Venue: Scientific Reports Authors: Sarah M. Alghamdi, Beth A. Sundberg, John P. Sundberg, Paul N. Schofield, Robert Hoehndorf Abstract Data are increasingly annotated with multiple ontologies to capture rich information about the features of the subject under investigation. Analysis may be performed over each ontology separately, but recently there has been a move to combine multiple ontologies to provide more powerful analytical possibilities. However, it is often not clear how to combine ontologies or how to assess or evaluate the potential design patterns available. Here we use a

Formal axioms in biomedical ontologies improve analysis and interpretation of associated data

Year: 2020 Venue: Bioinformatics Authors: Fatima Z. Smaili, Xin Gao, Robert Hoehndorf Abstract Motivation: There are now over 500 ontologies in the life sciences. Over the past years, significant resources have been invested into formalizing these biomedical ontologies. Formal axioms in ontologies have been developed and used to detect and ensure ontology consistency, find unsatisfiable classes, improve interoperability, guide ontology extension through the application of axiom-based design patterns, and encode domain background knowledge. At the same time, ontologies have extended their

Semantic similarity and machine learning with ontologies

Year: 2020 Venue: Briefings in Bioinformatics Authors: Maxat Kulmanov, Fatima Zohra Smaili, Xin Gao, Robert Hoehndorf DOI: 10.1093/bib/bbaa199 Abstract Ontologies have long been employed in the life sciences to formally represent and reason over domain knowledge and they are employed in almost every major biological database. Recently, ontologies are increasingly being used to provide background knowledge in similarity-based analysis and machine learning models. The methods employed to combine ontologies and machine learning are still novel and actively being developed. We provide an overview

Applied Ontology

Applied ontology, in our group, means using formal representation to make complex phenotypes, functions, and processes amenable to computational analysis across domains. The starting point is the standardization and curation of biological knowledge using ontologies built with explicit logical commitments, and the long-term goal is to produce representations that support both human curators and automated reasoners. Earlier work in this line concentrated on foundational ontologies, including the General Formal Ontology (GFO) and its biological extension GFO-Bio, and on a formal ontology of

PhenomeNet

Cross-species phenotype ontology and similarity network combining HPO, MPO, ZP and others; the substrate behind PhenomeNET-VP and DeepPheno. Get it GitHub: https://github.com/bio-ontology-research-group/phenomeblast ★ 1 Developed in projects CompleX: Variant Prioritization in Complex Disease Category: Ontologies & Resources

Units of Measurement Ontology (UO)

OBO Foundry ontology of units of measurement; aligned with QUDT and used across biomedical data standards. Get it GitHub: https://github.com/bio-ontology-research-group/unit-ontology ★ 23 Category: Ontologies & Resources

mOWL Tutorial

Step-by-step worked notebooks that demonstrate every embedding family in mOWL on protein-function, gene-disease and ontology-completion tasks. Get it GitHub: https://github.com/bio-ontology-research-group/mowl-tutorial ★ 8 Category: Teaching & Tutorials

Ontology Tutorial

Hands-on tutorial that walks new users through OWL, automated reasoning and ontology-aware data analysis; basis for the AI in Biomedicine summer school. Get it GitHub: https://github.com/bio-ontology-research-group/ontology-tutorial ★ 73 Category: Teaching & Tutorials

Machine Learning with Ontologies

Companion code and worked examples for the Briefings in Bioinformatics tutorial review; the most-starred repository in the group. Get it GitHub: https://github.com/bio-ontology-research-group/machine-learning-with-ontologies ★ 132 Category: Teaching & Tutorials

vec2SPARQL

Adds embedding-similarity functions to a SPARQL endpoint so that vector-space queries (k-nearest neighbours, cosine similarity) can be mixed with classical graph patterns. Get it GitHub: https://github.com/bio-ontology-research-group/vec2sparql ★ 14 Developed in projects Bio2Vec: Smart analytics infrastructure for the life sciences Category: Ontology Reasoning & Tooling

OntoFunc

Ontology-driven enrichment analysis that supports arbitrary OWL ontologies and full subsumption-aware aggregation, not only GO. Get it GitHub: https://github.com/bio-ontology-research-group/ontofunc Developed in projects Bio2Vec: Smart analytics infrastructure for the life sciences Category: Ontology Reasoning & Tooling

UNMIREOT

Identifies, diagnoses and semi-automatically repairs hidden contradictions and unsatisfiable classes introduced by partial imports (MIREOT) into biomedical ontologies. Get it GitHub: https://github.com/bio-ontology-research-group/UNMIREOT ★ 2 Developed in projects IBNSINA-QI: Integrating Biomedical Networks and Semantic Information for Neural network Analysis of Quantitative Information Category: Ontology Reasoning & Tooling

Onto2Graph

Generates entailment-aware graph projections of OWL ontologies suitable for downstream graph machine learning while preserving the axioms' deductive structure. Get it GitHub: https://github.com/bio-ontology-research-group/Onto2Graph ★ 12 Developed in projects IBNSINA-QI: Integrating Biomedical Networks and Semantic Information for Neural network Analysis of Quantitative Information Bio2Vec: Smart analytics infrastructure for the life sciences Category: Ontology Reasoning & Tooling

AberOWL

Ontology repository delivering OWL EL reasoning as a service: stores hundreds of bio-ontologies, exposes SPARQL with class-expression query expansion and powers semantic search over PubMed/PMC. Get it GitHub: https://github.com/bio-ontology-research-group/AberOWL ★ 10 Homepage: http://aber-owl.net Developed in projects Data integration and ontologies for microbial cell factories Category: Ontology Reasoning & Tooling

Interpretable Learning

Generates interpretable symbolic rules from learned representations over biomedical knowledge bases. Get it GitHub: https://github.com/bio-ontology-research-group/interpretable-learning ★ 5 Developed in projects Towards sound, complete, and explainable machine learning with biomedical ontologies (CRG11) Category: Ontology Embedding & Machine Learning

EL2Box

Box-shaped geometric embeddings for EL++ that strengthen the topological guarantees of EL Embeddings. Get it GitHub: https://github.com/bio-ontology-research-group/EL2Box_embedding ★ 2 Developed in projects IBNSINA-QI: Integrating Biomedical Networks and Semantic Information for Neural network Analysis of Quantitative Information Category: Ontology Embedding & Machine Learning

Walking RDF and OWL

Original feature-learning method over RDF graphs and OWL ontologies via biased random walks; the seed implementation for many later embedding methods including OWL2Vec*. Get it GitHub: https://github.com/bio-ontology-research-group/walking-rdf-and-owl ★ 47 Developed in projects Bio2Vec: Smart analytics infrastructure for the life sciences Category: Ontology Embedding & Machine Learning

DL2Vec

Encodes description-logic axioms as a directed graph and learns embeddings via random walks; widely used for downstream gene-disease and protein-function prediction. Get it GitHub: https://github.com/bio-ontology-research-group/DL2Vec ★ 20 Developed in projects Bio2Vec: Smart analytics infrastructure for the life sciences Category: Ontology Embedding & Machine Learning

Onto2Vec

Representation learning for ontologies and their annotations by treating logical axioms as natural-language sentences; predecessor of OPA2Vec. Get it GitHub: https://github.com/bio-ontology-research-group/onto2vec ★ 21 Developed in projects Bio2Vec: Smart analytics infrastructure for the life sciences Category: Ontology Embedding & Machine Learning

OPA2Vec

Combines ontology axioms with associated annotation properties (labels, synonyms, definitions) into a single corpus, then trains Word2Vec to produce semantically rich vectors for ontology classes. Get it GitHub: https://github.com/bio-ontology-research-group/opa2vec ★ 37 Developed in projects Bio2Vec: Smart analytics infrastructure for the life sciences CompleX: Variant Prioritization in Complex Disease Category: Ontology Embedding & Machine Learning

DELE

Deductive EL embeddings: enrich training data with the deductive closure of an ontology before learning, so embeddings recover entailment rather than only asserted axioms. Get it GitHub: https://github.com/bio-ontology-research-group/DELE ★ 2 Developed in projects IBNSINA-QI: Integrating Biomedical Networks and Semantic Information for Neural network Analysis of Quantitative Information Category: Ontology Embedding & Machine Learning

catE

Category-theoretic, lattice-preserving embedding of ALC description-logic ontologies that retains the consequence-closure semantics of the original theory. Get it GitHub: https://github.com/bio-ontology-research-group/catE ★ 3 Developed in projects IBNSINA-QI: Integrating Biomedical Networks and Semantic Information for Neural network Analysis of Quantitative Information Category: Ontology Embedding & Machine Learning

EL Embeddings

Reference implementation of geometric embeddings for the EL++ description logic, the predecessor of GeometrE and BoxSquaredEL. Preserves subsumption reasoning by mapping classes to convex regions. Get it GitHub: https://github.com/bio-ontology-research-group/el-embeddings ★ 28 Developed in projects IBNSINA-QI: Integrating Biomedical Networks and Semantic Information for Neural network Analysis of Quantitative Information Category: Ontology Embedding & Machine Learning

mOWL

Python library for machine learning with biomedical ontologies. Unifies projection-, axiom- and geometric-embedding methods (EL Embeddings, ELBE, BoxSquaredEL, OWL2Vec*, DL2Vec, OPA2Vec) behind one API, with first-class OWLAPI access and PyTorch integration. Get it GitHub: https://github.com/bio-ontology-research-group/mowl ★ 91 Homepage: https://mowl.readthedocs.io Developed in projects IBNSINA-QI: Integrating Biomedical Networks and Semantic Information for Neural network Analysis of Quantitative Information Towards sound, complete, and explainable machine learning with biomedical ontologies

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 — research illustration

Data integration and ontologies for microbial cell factories

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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 Microbial Cell Factories initiative at KAUST's Computational Bioscience Research Center (CBRC, now dissolved), led by Vladimir Bajic with Hoehndorf as CoI
Bio2Vec: Smart analytics infrastructure for the life sciences — research illustration

Bio2Vec: Smart analytics infrastructure for the life sciences

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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 — research illustration

Improvement of genetic variant prioritization technology

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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 — research illustration

CompleX: Variant Prioritization in Complex Disease

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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 — research illustration

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

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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 — research illustration

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

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

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

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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