Ontology engineering

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

Integration of knowledge for personalized medicine: a pharmacogenomics case-study

Year: 2012 Venue: Proceedings of the Virtual Physiological Human Conference 2012 (VPH2012) Authors: Robert Hoehndorf, Michel Dumontier, Georgios V. Gkoutos

Investigation of the fundamental strategy for interoperability of description of biological measurements

Year: 2011 Venue: Proceedings of the Second International Conference on Biomedical Ontology Authors: Hiroshi Masuya, Georgios V. Gkoutos, Nobuhiko Tanaka, Kazunori Waki, Yoshihiro Okuda, Tatsuya Kushida, Norio Kobayashi, Koji Doi, Kouji Kozaki, Robert Hoehndorf, Shigeharu Wakana, Tetsuro Toyoda, Riichiro Mizoguchi Abstract Aiming the facilitation of the advanced integration of measurement data across various biological experiments, we have investigated the fundamental methodology to expand the Phenotypic Quality Ontology (PATO) commonly used for descriptions of biological phenotypes with the

Vec2SPARQL: integrating SPARQL queries and knowledge graph embeddings

Year: 2018 Venue: Proceedings of the 11th International Conference Semantic Web Applications and Tools for Life Sciences, SWAT4LS 2018, Antwerp, Belgium, December 3-6, 2018. Authors: Maxat Kulmanov, Senay Kafkas, Andreas Karwath, Alexander Malic, Georgios V. Gkoutos, Michel Dumontier, Robert Hoehndorf Acknowledged projects ccf-microbial-cell-factories crg-bio2vec

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 Acknowledged projects ccf-microbial-cell-factories crg-complex-variant-prioritization crg-ibnsina-qi

SPARQL2OWL: Towards Bridging the Semantic Gap Between RDF and OWL

Year: 2016 Venue: Proceedings of the Joint International Conference on Biological Ontology and BioCreative, Corvallis, Oregon, United States, August 1-4, 2016. Authors: Mona Alshahrani, Hussein Almashouq, Robert Hoehndorf Abstract Several large databases in biology are now making theirinformation available through the Resource Description Framework(RDF). RDF can be used for large datasets and provides agraph-based semantics. The Web Ontology Language (OWL),another Semantic Web standard, provides a more formal, model-theoretic semantics. While some approaches combine RDF andOWL, for example for

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 Abstract Reasoning over biomedical ontologies using their OWL semantics has traditionally been a challenging task due to the high theoretical complexity of OWL-based automated reasoning. As a consequence, ontology repositories, as well as

AberOWL: an ontology portal with OWL EL reasoning

Year: 2015 Venue: Proceedings of International Conference on Biomedical Ontologies (ICBO) Authors: Luke Slater, Georgios Gkoutos, Paul N. Schofield, Robert Hoehndorf Abstract The field of biological and biomedical science quickly generate large quantities of data and knowledge; often, domain knowledge is formalised using ontologies expressed in the Web Ontology Language (OWL). Ontology repositories such as Bioportal and Ontobee have been an important infrastructural component for managing ontologies, specifically to search, browse and download ontologies over the Web. We present the AberOWL

Using Aber-OWL for fast and scalable reasoning over BioPortal ontologies

Year: 2015 Venue: Proceedings of International Conference on Biomedical Ontologies (ICBO) Authors: Luke Slater, Georgios Gkoutos, Paul N. Schofield, Robert Hoehndorf Abstract Reasoning over biomedical ontologies using their OWL semantics has traditionally been a challenging task due to the high theoretical complexity of OWL-based automated reasoning. As a consequence, ontology repositories, as well as most other tools utilizing ontologies, either provide access to ontologies without use of automated reasoning, or limit the number of ontologies for which automated reasoning-based access is

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

Evaluating Different Methods for Semantic Reasoning Over Ontologies

Year: 2023 Venue: Joint Proceedings of Scholarly QALD 2023 and SemREC 2023 co-located with 22nd International Semantic Web Conference ISWC 2023, Athens, Greece, November 6-10, 2023 Authors: Fernando Zhapa-Camacho, Robert Hoehndorf

An integrative, translational approach to understanding rare and orphan genetically based diseases

Year: 2013 Venue: Interface Focus Authors: Robert Hoehndorf, Paul N. Schofield, Georgios V. Gkoutos DOI: 10.1098/rsfs.2012.0055 Abstract PhenomeNet is an approach for integrating phenotypes across species and identifying candidate genes for genetic diseases based on the similarity between a disease and animal model phenotypes. In contrast to 'guilt-by-association' approaches, PhenomeNet relies exclusively on the comparison of phenotypes to suggest candidate genes, and can, therefore, be applied to study the molecular basis of rare and orphan diseases for which the molecular basis is unknown

Chapter Four - The Neurobehavior Ontology: An Ontology for Annotation and Integration of Behavior and Behavioral Phenotypes

Year: 2012 Venue: Bioinformatics of Behavior: Part 1 Authors: Georgios V. Gkoutos, Paul N. Schofield, Robert Hoehndorf DOI: 10.1016/B978-0-12-388408-4.00004-6 Abstract Abstract In recent years, considerable advances have been made toward our understanding of the genetic architecture of behavior and the physical, mental, and environmental influences that underpin behavioral processes. The provision of a method for recording behavior-related phenomena is necessary to enable integrative and comparative analyses of data and knowledge about behavior. The neurobehavior ontology facilitates the

The Units Ontology: a tool for integrating units of measurement in science

Year: 2012 Venue: Database Authors: Georgios V. Gkoutos, Paul N. Schofield, Robert Hoehndorf Abstract Units are basic scientific tools that render meaning to numerical data. Their standardization and formalization caters for the report, exchange, process, reproducibility and integration of quantitative measurements. Ontologies are means that facilitate the integration of data and knowledge allowing interoperability and semantic information processing between diverse biomedical resources and domains. Here, we present the Units Ontology (UO), an ontology currently being used in many scientific

An infrastructure for ontology-based information systems in biomedicine: RICORDO case study

Venue: Bioinformatics Authors: Sarala M. Wimalaratne, Pierre Grenon, Robert Hoehndorf, Georgios V. Gkoutos, Bernard de Bono Abstract Summary: The article presents an infrastructure for supporting the semantic interoperability of biomedical resources based on the management (storing and inference-based querying) of their ontology-based annotations. This infrastructure consists of: (i) a repository to store and query ontology-based annotations; (ii) a knowledge base server with an inference engine to support the storage of and reasoning over ontologies used in the annotation of resources; (iii)

Semantic integration of physiology phenotypes with an application to the Cellular Phenotype Ontology

Year: 2012 Venue: Bioinformatics Authors: Robert Hoehndorf, Midori A. Harris, Heinrich Herre, Gabriella Rustici, Georgios V. Gkoutos DOI: 10.1093/bioinformatics/bts250 Abstract Motivation: The systematic observation of phenotypes has become a crucial tool of functional genomics, and several large international projects are currently underway to identify and characterize the phenotypes that are associated with genotypes in several species. To integrate phenotype descriptions within and across species, phenotype ontologies have been developed. Applying ontologies to unify phenotype descriptions

Logical Gene Ontology Annotations (GOAL): exploring gene ontology annotations with OWL

Venue: Journal of Biomedical Semantics Authors: Simon Jupp, Robert Stevens, Robert Hoehndorf Abstract MOTIVATION:Ontologies such as the Gene Ontology (GO) and their use in annotations make cross species comparisons of genes possible, along with a wide range of other analytical activities. The bio-ontologies community, in particular the Open Biomedical Ontologies (OBO) community, have provided many other ontologies and an increasingly large volume of annotations of gene products that can be exploited in query and analysis. As many annotations with different ontologies centre upon gene products

Ontology design patterns to disambiguate relations between genes and gene products in GENIA

Venue: Journal of Biomedical Semantics Authors: Robert Hoehndorf, Axel-Cyrille Ngonga Ngomo, Sampo Pyysalo, Tomoko Ohta, Anika Oellrich, Dietrich Rebholz-Schuhmann DOI: 10.1186/2041-1480-2-S5-S1 Abstract MOTIVATION:Annotated reference corpora play an important role in biomedical information extraction. A semantic annotation of the natural language texts in these reference corpora using formal ontologies is challenging due to the inherent ambiguity of natural language. The provision of formal definitions and axioms for semantic annotations offers the means for ensuring consistency as well as

The RICORDO approach to semantic interoperability for biomedical data and models: strategy, standards and solutions.

Venue: BMC Research Notes Authors: Bernard de Bono, Robert Hoehndorf, Sarala Wimalaratne, Georgios V. Gkoutos, Pierre Grenon Abstract BACKGROUND:The practice and research of medicine generates considerable quantities of data and model resources (DMRs). Although in principle biomedical resources are re-usable, in practice few can currently be shared. In particular, the clinical communities in physiology and pharmacology research, as well as medical education, (i.e. PPME communities) are facing considerable operational and technical obstacles in sharing data and models.FINDINGS:We outline the

Interoperability between biomedical ontologies through relation expansion, upper-level ontologies and automatic reasoning

Venue: PLOS ONE Authors: Robert Hoehndorf, Michel Dumontier, Anika Oellrich, Dietrich Rebholz-Schuhmann, Paul N. Schofield, Georgios V. Gkoutos Abstract Researchers design ontologies as a means to accurately annotate and integrate experimental data across heterogeneous and disparate data- and knowledge bases. Formal ontologies make the semantics of terms and relations explicit such that automated reasoning can be used to verify the consistency of knowledge. However, many biomedical ontologies do not sufficiently formalize the semantics of their relations and are therefore limited with respect

A common layer of interoperability for biomedical ontologies based on OWL EL

Venue: Bioinformatics Authors: Robert Hoehndorf, Michel Dumontier, Anika Oellrich, Sarala Wimalaratne, Dietrich Rebholz-Schuhmann, Paul N. Schofield, Georgios V. Gkoutos Abstract Motivation: Ontologies are essential in biomedical research due to their ability to semantically integrate content from different scientific databases and resources. Their application improves capabilities for querying and mining biological knowledge. An increasing number of ontologies is being developed for this purpose, and considerable effort is invested into formally defining them in order to represent their

Interoperability between phenotype and anatomy ontologies

Venue: Bioinformatics Authors: Robert Hoehndorf, Anika Oellrich, Dietrich Rebholz-Schuhmann Abstract Motivation: Phenotypic information is important for the analysis of the molecular mechanisms underlying disease. A formal ontological representation of phenotypic information can help to identify, interpret and infer phenotypic traits based on experimental findings. The methods that are currently used to represent data and information about phenotypes fail to make the semantics of the phenotypic trait explicit and do not interoperate with ontologies of anatomy and other domains. Therefore

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

BioHackathon 2015: Semantics of data for life sciences and reproducible research

Year: 2020 Venue: F1000Research Authors: Rutger A. Vos, Toshiaki Katayama, Hiroyuki Mishima, Shin Kawano, Shuichi Kawashima, Jin-Dong Kim, Yuki Moriya, Toshiaki Tokimatsu, Atsuko Yamaguchi, Yasunori Yamamoto, Hongyan Wu, Peter Amstutz, Erick Antezana, Nobuyuki P. Aoki, Kazuharu Arakawa, Jerven T. Bolleman, Evan Bolton, Raoul J. P. Bonnal, Hidemasa Bono, Kees Burger, Hirokazu Chiba, Kevin B. Cohen, Eric W. Deutsch, Jesualdo T. Fern\'andez-Breis, Gang Fu, Takatomo Fujisawa, Atsushi Fukushima, Alexander Garc\'\ia, Naohisa Goto, Tudor Groza, Colin Hercus, Robert Hoehndorf, Kotone Itaya, Nick Juty

Klarigi: Characteristic explanations for semantic biomedical data

Year: 2023 Venue: Computers in Biology and Medicine Authors: Luke T. Slater, John A. Williams, Paul N. Schofield, Sophie Russell, Samantha C. Pendleton, Andreas Karwath, Hilary Fanning, Simon Ball, Robert Hoehndorf, Georgios V. Gkoutos DOI: 10.1016/j.compbiomed.2022.106425 Abstract Annotation of biomedical entities with ontology classes provides for formal semantic analysis and mobilisation of background knowledge in determining their relationships. To date, enrichment analysis has been routinely employed to identify classes that are over-represented in annotations across sets of groups, such

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

DESM: portal for microbial knowledge exploration systems

Year: 2016 Venue: Nucleic Acids Research Authors: Adil Salhi, Magbubah Essack, Aleksandar Radovanovic, Benoit Marchand, Salim Bougouffa, Andre Antunes, Marta Filipa Simoes, Feras F. Lafi, Olaa A. Motwalli, Ameerah Bokhari, Tariq Malas, Soha Al Amoudi, Ghofran Othum, Intikhab Allam, Katsuhiko Mineta, Xin Gao, Robert Hoehndorf, John A. C. Archer, Takashi Gojobori, Vladimir B. Bajic DOI: 10.1093/nar/gkv1147 Abstract Microorganisms produce an enormous variety of chemical compounds. It is of general interest for microbiology and biotechnology researchers to have means to explore information about

Combining lexical and context features for automatic ontology extension

Year: 2020 Venue: Journal of Biomedical Semantics Authors: Sara Althubaiti, Senay Kafkas, Marwa Abdelhakim, Robert Hoehndorf Acknowledged projects ccf-microbial-cell-factories crg-bio2vec

DeepGOWeb: fast and accurate protein function prediction on the (Semantic) Web

Year: 2021 Venue: Nucleic Acids Research Authors: Maxat Kulmanov, Fernando Zhapa-Camacho, Robert Hoehndorf DOI: 10.1093/nar/gkab373 Abstract Understanding the functions of proteins is crucial to understand biological processes on a molecular level. Many more protein sequences are available than can be investigated experimentally. DeepGOPlus is a protein function prediction method based on deep learning and sequence similarity. DeepGOWeb makes the prediction model available through a website, an API, and through the SPARQL query language for interoperability with databases that rely on Semantic

Usage of cell nomenclature in biomedical literature

Year: 2017 Venue: BMC Bioinformatics Authors: \cSenay Kafkas, Sirarat Sarntivijai, Robert Hoehndorf DOI: 10.1186/s12859-017-1978-0 Abstract Cell lines and cell types are extensively studied in biomedical research yielding to a significant amount of publications each year. Identifying cell lines and cell types precisely in publications is crucial for science reproducibility and knowledge integration. There are efforts for standardisation of the cell nomenclature based on ontology development to support FAIR principles of the cell knowledge. However, it is important to analyse the usage of cell

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

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

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

An open source knowledge graph ecosystem for the life sciences

Year: 2024 Venue: Scientific Data Authors: Tiffany J. Callahan, Ignacio J. Tripodi, Adrianne L. Stefanski, Luca Cappelletti, Sanya B. Taneja, Jordan M. Wyrwa, Elena Casiraghi, Nicolas A. Matentzoglu, Justin Reese, Jonathan C. Silverstein, Charles Tapley Hoyt, Richard D. Boyce, Scott A. Malec, Deepak R. Unni, Marcin P. Joachimiak, Peter N. Robinson, Christopher J. Mungall, Emanuele Cavalleri, Tommaso Fontana, Giorgio Valentini, Marco Mesiti, Lucas A. Gillenwater, Brook Santangelo, Nicole A. Vasilevsky, Robert Hoehndorf, Tellen D. Bennett, Patrick B. Ryan, George Hripcsak, Michael G. Kahn

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

Neuro-symbolic representation learning on biological knowledge graphs

Year: 2017 Venue: Bioinformatics Authors: Mona Alshahrani, Mohammad Asif Khan, Omar Maddouri, Akira R. Kinjo, Nuria Queralt-Rosinach, Robert Hoehndorf Abstract Motivation: Biological data and knowledge bases increasingly rely on Semantic Web technologies and the use of knowledge graphs for data integration, retrieval and federated queries. In the past years, feature learning methods that are applicable to graph-structured data are becoming available, but have not yet widely been applied and evaluated on structured biological knowledge. Results: We develop a novel method for feature learning on

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

Aber-OWL: a framework for ontology-based data access in biology

Year: 2015 Venue: BMC Bioinformatics Authors: Robert Hoehndorf, Luke Slater, Paul N Schofield, Georgios V Gkoutos Abstract Background: Many ontologies have been developed in biology and these ontologies increasingly contain large volumes of formalized knowledge commonly expressed in the Web Ontology Language (OWL). Computational access to the knowledge contained within these ontologies relies on the use of automated reasoning. Results: We have developed the Aber-OWL infrastructure that provides reasoning services for bio-ontologies. Aber-OWL consists of an ontology repository, a set of web

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

BioHackathon series in 2013 and 2014: improvements of semantic interoperability in life science data and services

Year: 2019 Venue: F1000Research Authors: T Katayama, S Kawashima, G Micklem, S Kawano, JD Kim, S Kocbek, S Okamoto, Y Wang, H Wu, A Yamaguchi, Y Yamamoto, E Antezana, KF Aoki-Kinoshita, K Arakawa, M Banno, J Baran, JT Bolleman, RJP Bonnal, H Bono, JT Fernandez-Breis, R Buels, MP Campbell, H Chiba, PJA Cock, KB Cohen, M Dumontier, T Fujisawa, T Fujiwara, L Garcia, P Gaudet, E Hattori, R Hoehndorf, K Itaya, M Ito, D Jamieson, S Jupp, N Juty, A Kalderimis, F Kato, H Kawaji, T Kawashima, AR Kinjo, Y Komiyama, M Kotera, T Kushida, J Malone, M Matsubara, S Mizuno, S Mizutani, H Mori, Y Moriya, K

Ontology engineering

Ontology engineering and semantic interoperability address the practical problem of turning hundreds of independently developed biomedical ontologies into an infrastructure that can be queried, reasoned over, and combined at scale. Our group designs architectures for processing large, heterogeneous datasets using Semantic Web standards, with a particular emphasis on bringing automated reasoning into routine data-access workflows. The angle taken at KAUST is engineering-led: we treat ontology-based data access as a service that must be fast enough for interactive use, expressive enough to

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 Phenotype ontologies and translational research (Cambridge / Aberystwyth) 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

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 Phenotype ontologies and translational research (Cambridge / Aberystwyth) 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

Safana Bakheet

Safana Bakheet completed her MSc in Bioengineering at KAUST in 2025 under the supervision of Robert Hoehndorf. Her work was carried out in collaboration with Fernando Zhapa-Camacho and developed methods for prioritising disease-causing genes by combining ontology-based phenotype representations with supervised knowledge graph embeddings. Her thesis, An Inductive, Supervised Gene-Disease Associations Method, introduces a ranking model that scores candidate disease-causing genes based on the similarity of their associated phenotypes, represented in the Mammalian Phenotype Ontology (MP) and the

Md Nurul Muttakin

Md Nurul Muttakin completed his MSc in Computer Science at KAUST in 2023 under the supervision of Robert Hoehndorf. His research focused on machine learning methods for predicting protein function from three-dimensional structural information. His thesis, 3D conformation-based protein function prediction, investigated how structural information should be exploited by deep learning models in light of the availability of near-experimental-accuracy structures from AlphaFold. Almost all existing structure-aware protein function predictors combine graph neural networks (GNNs) operating on 2D

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

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

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
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