Applied Ontology

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

Argumentation to Represent and Reason over Biological Systems

Year: 2012 Venue: Proceedings of the 3rd International Conference on Information Technology in Bio- and Medical Informatics (ITBAM 2012) Authors: Adam Wyner, Luke Riley, Robert Hoehndorf, Samuel Croset

Exploring Gene Ontology Annotations with OWL

Year: 2011 Venue: Proceedings of the 13th Bio-Ontology Meeting Authors: Simon Jupp, Robert Stevens, Robert Hoehndorf Abstract 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 activities. Tools, such as AmiGO, allow exploration of genes based on their GO annotations. This human driven exploration and querying of GO is obviously useful, but by taking advantage of the ontological representation we can use these annotations to create a rich polyhierarchy of gene products for enhanced querying

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

Higgs bosons, mars missions, and unicorn delusions: How to deal with terms of dubious reference in scientific ontologies

Year: 2011 Venue: Proceedings of the Second International Conference on Biomedical Ontology Authors: Stefan Schulz, Mathias Brochhausen, Robert Hoehndorf Abstract Realist ontologies claim to represent what exists. Scientific discourse, however, often contains non-referring terms when describing hypotheses, plans, or ideas. We present a framework in which a realist ontology is embedded in an description logics theory, which is indifferent regarding the existence of class members, and which may include representational units for representing various kinds of non-referring terms. Using a taxonomy

Lattice-Preserving ALC Ontology Embeddings

Year: 2024 Venue: Neural-Symbolic Learning and Reasoning Authors: Fernando Zhapa-Camacho, Robert Hoehndorf DOI: 10.1007/978-3-031-71167-1_19

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

Positive-Unlabeled Learning with Adversarial Data Augmentation for Knowledge Graph Completion

Year: 2022 Venue: Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence Authors: Zhenwei Tang, Shichao Pei, Zhao Zhang, Yongchun Zhu, Fuzhen Zhuang, Robert Hoehndorf, Xiangliang Zhang DOI: 10.24963/ijcai.2022/312 Abstract Most real-world knowledge graphs (KG) are far from complete and comprehensive. This problem has motivated efforts in predicting the most plausible missing facts to complete a given KG, i.e., knowledge graph completion (KGC). However, existing KGC methods suffer from two main issues, 1) the false negative issue, i.e., the sampled negative

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

Ontology-Based Concept Recognition by Using Word Embeddings

Year: 2018 Venue: Bio-Ontologies COSI Authors: Sara Althubaiti, Senay Kafkas, Robert Hoehndorf

Semi-Supervised Entity Alignment via Knowledge Graph Embedding with Awareness of Degree Difference

Year: 2019 Venue: The World Wide Web Conference Authors: Shichao Pei, Lu Yu, Robert Hoehndorf, Xiangliang Zhang DOI: 10.1145/3308558.3313646 Abstract Entity alignment associates entities in different knowledge graphs if they are semantically same, and has been successfully used in the knowledge graph construction and connection. Most of the recent solutions for entity alignment are based on knowledge graph embedding, which maps knowledge entities in a low-dimension space where entities are connected with the guidance of prior aligned entity pairs. The study in this paper focuses on two

Integrating phenotype ontologies with PhenomeNET

Year: 2016 Venue: Proceedings of Ontology Matching Workshop 2016 Authors: Miguel Rodriguez-Garcia, Georgios V. Gkoutos, Paul N. Schofield, Robert Hoehndorf Abstract PhenomeNET is a system for disease gene prioritization that includes as one of its components an ontology designed to integrate phenotype ontologies. While not applicable to matching arbitrary ontologies, PhenomeNET can be used to identify related phenotypes in different species, including human, mouse, zebrafish, nematode worm, fruit fly, and yeast. Here, we apply the PhenomeNET to identify related classes from four phenotype and

Enhancing Geometric Ontology Embeddings for ^++ with Negative Sampling and Deductive Closure Filtering

Year: 2024 Venue: Neural-Symbolic Learning and Reasoning Authors: Olga Mashkova, Fernando Zhapa-Camacho, Robert Hoehndorf DOI: 10.1007/978-3-031-71167-1_18

EL Embeddings: Geometric construction of models for the Description Logic EL++

Year: 2019 Venue: Proceedings of IJCAI 2019 Authors: Maxat Kulmanov, Wang Liu-Wei, Yuan Yan, Robert Hoehndorf Abstract An embedding is a function that maps entities from one algebraic structure into another while preserving certain characteristics. Embeddings are being used successfully for mapping relational data or text into vector spaces where they can be used for machine learning, similarity search, or similar tasks. We address the problem of finding vector space embeddings for theories in the Description Logic EL++ that are also models of the TBox. To find such embeddings, we define an

Ontology based mining of pathogen-disease associations from literature

Year: 2018 Venue: Bio-Ontologies COSI Authors: Senay Kafkas, Robert Hoehndorf

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

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

From Axioms over Graphs to Vectors, and Back Again: Evaluating the Properties of Graph-based Ontology Embeddings

Year: 2023 Venue: Proceedings of the 17th International Workshop on Neural-Symbolic Learning and Reasoning, La Certosa di Pontignano, Siena, Italy, July 3-5, 2023 Authors: Fernando Zhapa-Camacho, Robert Hoehndorf

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 Acknowledged projects ccf-algorithms-biotech-biomedical coe-genai-bcb crg-ibnsina-qi crg-functional-metagenomics crg-coral-climate-warming crg-explainable-ml-ontologies

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

Semantic Systems Biology: Formal Knowledge Representation in Systems Biology for Model Construction, Retrieval, Validation and Discovery

Year: 2013 Venue: Systems Biology Authors: Michel Dumontier, Leonid L Chepelev, Robert Hoehndorf Abstract With the publication of the human genome, scientists worldwide opened champagne and let out a collective cheer for progress in biology. After all, the untold number of interactions of tens of thousands of genes, a greater number of their products and product derivatives, and tens of thousands of chemicals came much closer to complete characterization. Paradoxically however, while individual efforts produced important biological results, an integrated view of biology from systems

Evaluation and Cross-Comparison of Lexical Entities of Biological Interest (LexEBI)

Year: 2013 Venue: PLoS ONE Authors: Dietrich Rebholz-Schuhmann, Jee-Hyub Kim, Ying Yan, Abhishek Dixit, Caroline Friteyre, Robert Hoehndorf, Rolf Backofen, Ian Lewin Abstract Motivation: Biomedical entities, their identifiers and names, are essential in the representation of biomedical facts and knowledge. In the same way, the complete set of biomedical and chemical terms, i.e. the biomedical "term space" (the "Lexeome"), forms a key resource to achieve the full integration of the scientific literature with biomedical data resources: any identified named entity can immediately be normalized to

Representing physiological processes and their participants with PhysioMaps

Year: 2013 Venue: Journal of Biomedical Semantics Authors: Daniel Cook, Maxwell Neal, Robert Hoehndorf, Georgios Gkoutos, John Gennari DOI: 10.1186/2041-1480-4-S1-S2 Abstract BACKGROUND:As the number and size of biological knowledge resources for physiology grows, researchers need improved tools for searching and integrating knowledge and physiological models. Unfortunately, current resources--databases, simulation models, and knowledge bases, for example--are only occasionally and idiosyncratically explicit about the semantics of the biological entities and processes that they describe

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

Evaluation of research in biomedical ontologies

Year: 2012 Venue: Briefings in Bioinformatics Authors: Robert Hoehndorf, Michel Dumontier, Georgios V. Gkoutos Abstract Ontologies are now pervasive in biomedicine, where they serve as a means to standardize terminology, to enable access to domain knowledge, to verify data consistency and to facilitate integrative analyses over heterogeneous biomedical data. For this purpose, research on biomedical ontologies applies theories and methods from diverse disciplines such as information management, knowledge representation, cognitive science, linguistics and philosophy. Depending on the desired

Ontology-based cross-species integration and analysis of Saccharomyces cerevisiae phenotypes

Year: 2011 Venue: Proceedings of the 3rd Workshop for Ontologies in Biomedicine and Life sciences (OBML) Authors: Georgios V. Gkoutos, Robert Hoehndorf Abstract Ontologies are widely used in the biomedical community for annotation and integration of databases. Formal definitions can relate classes from different ontologies and thereby integrate data across different levels of granularity, domains and species. We have applied this methodology to the Ascomycete Phenotype Ontology (APO), enabling the reuse of various orthogonal ontologies and we have converted the phenotype associated data found

Towards Improving Phenotype Representation in OWL

Year: 2011 Venue: Proceedings of the 3rd Workshop for Ontologies in Biomedicine and Life sciences (OBML) Authors: Frank Loebe, Frank Stumpf, Robert Hoehndorf, Heinrich Herre Abstract Phenotype ontologies are used in species-specific databases for the annotation of mutagenesis experiments and to characterize hu- man diseases. The Entity-Quality (EQ) formalism is a means to describe complex phenotypes based on one or more affected en- tities and a quality. EQ-based definitions have been developed for many phenotype ontologies, including the Human and Mammalian Phenotype ontologies. We analyze

Quantitative comparison of mapping methods between Human and Mammalian Phenotype Ontology

Year: 2011 Venue: Proceedings of the 3rd Workshop for Ontologies in Biomedicine and Life sciences (OBML) Authors: Anika Oellrich, Robert Hoehndorf, Georgios V. Gkoutos, Dietrich Rebholz-Schuhmann Abstract Researchers use animal studies to better understand human diseases. In recent years, large-scale phenotype studies such as Phenoscape and EuroPhenome have been initiated to identify genetic causes of a species phenome. Species-specific phenotype ontologies are required to capture and report about all findings and to automatically infer results relevant to human diseases. The integration of

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

Linking PharmGKB to phenotype studies and animal models of disease for drug repurposing

Venue: Pacific Symposium on Biocomputing (PSB) Authors: Robert Hoehndorf, Anika Oellrich, Dietrich Rebholz-Schuhmann, Paul N. Schofield, Georgios V. Gkoutos Abstract The investigation of phenotypes in model organisms has the potential to reveal the molecular mechanisms underlying disease. The large-scale comparative analysis of phenotypes across species can reveal novel associations between genotypes and diseases. We use the PhenomeNET network of phenotypic similarity to suggest genotype--disease association, combine them with drug--gene associations available from the PharmGKB database, and

OBML - Ontologies in Biomedicine and Life Sciences

Venue: Journal of Biomedical Semantics Authors: Heinrich Herre, Robert Hoehndorf, Janet Kelso, Frank Loebe, Stefan Schulz DOI: 10.1186/2041-1480-2-S4-I1 Abstract The OBML 2010 workshop, held at the University of Mannheim on September 9-10, 2010, is the 2nd in a series of meetings organized by the Working Group "Ontologies in Biomedicine and Life Sciences" of the German Society of Computer Science (GI) and the German Society of Medical Informatics, Biometry and Epidemiology (GMDS). Integrating, processing and applying the rapidly expanding information generated in the life sciences -- from

New approaches to the representation and analysis of phenotype knowledge in human diseases and their animal models

Venue: Briefings in Functional Genomics Authors: Paul N. Schofield, John P. Sundberg, Robert Hoehndorf, Georgios V. Gkoutos Abstract The systematic investigation of the phenotypes associated with genotypes in model organisms holds the promise of revealing genotype–phenotype relations directly and without additional, intermediate inferences. Large-scale projects are now underway to catalog the complete phenome of a species, notably the mouse. With the increasing amount of phenotype information becoming available, a major challenge that biology faces today is the systematic analysis of this

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

PIDO: The Primary Immunodeficiency Disease Ontology

Venue: Bioinformatics Authors: Nico Adams, Robert Hoehndorf, Georgios V. Gkoutos, Gesine Hansen, Christian Hennig Abstract Motivation: Primary Immunodeficiency Diseases (PIDs) are Mendelian conditions of high phenotypic complexity and low incidence. They usually manifest in toddlers and infants, although they can also occur much later in life. Information about PIDs is often widely scattered throughout the clinical as well as the research literature and hard to find for both generalists as well as experienced clinicians. Semantic Web technologies coupled to clinical information systems can go

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

The RNA Ontology (RNAO): An Ontology for Integrating RNA Sequence and Structure Data

Venue: Applied Ontology Authors: Robert Hoehndorf, Colin Batchelor, Thomas Bittner, Michel Dumontier, Karen Eilbeck, Rob Knight, Chris J. Mungall, Jane S. Richardson, Jesse Stombaugh, Eric Westhof, Craig L. Zirbel, Neocles B. Leontis Abstract Biomedical Ontologies integrate diverse biomedical data and enable intelligent data-mining and help translate basic research into useful clinical knowledge. We present the RNA Ontology (RNAO), an ontology for integrating diverse RNA data, including RNA sequences and sequence alignments, three-dimensional structures, and biochemical and functional data

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

Lattice-Based ALC Ontology Embeddings With Saturation

Year: 2025 Venue: Neurosymbolic Artificial Intelligence Authors: Fernando Zhapa-Camacho, Robert Hoehndorf DOI: 10.1177/29498732251340186 Abstract Generating vector representations (embeddings) of OWL ontologies is a growing task due to its applications in predicting missing facts and knowledge-enhanced learning in fields such as bioinformatics. The underlying semantics of OWL ontologies are expressed using Description Logics (DLs). Initial approaches to generate embeddings relied on constructing a graph out of ontologies, neglecting the semantics of the logic therein. Recent semantic

mOWL: Python library for machine learning with biomedical ontologies

Year: 2023 Venue: Bioinformatics Authors: Fernando Zhapa-Camacho, Maxat Kulmanov, Robert Hoehndorf DOI: 10.1093/bioinformatics/btac811 Abstract Supplementary data are available at Bioinformatics online.

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

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

Evaluating semantic similarity methods for comparison of text-derived phenotype profiles

Year: 2022 Venue: BMC Medical Informatics and Decision Making Authors: Luke T. Slater, Sophie Russell, Silver Makepeace, Alexander Carberry, Andreas Karwath, John A. Williams, Hilary Fanning, Simon Ball, Robert Hoehndorf, Georgios V. Gkoutos DOI: 10.1186/s12911-022-01770-4 Abstract Abstract Background Semantic similarity is a valuable tool for analysis in biomedicine. When applied to phenotype profiles derived from clinical text, they have the capacity to enable and enhance ‘patient-like me’ analyses, automated coding, differential diagnosis, and outcome prediction. While a large body of work

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

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 Abstract Semantic similarity is a useful approach for comparing patient phenotypes, and holds the potential of an effective method for exploiting text-derived phenotypes for differential diagnosis, text and document classification, and outcome prediction. While approaches for context disambiguation are commonly used in text mining applications, forming a standard component of information extraction pipelines, their effects on semantic

A fast, accurate, and generalisable heuristic-based negation detection algorithm for clinical text

Year: 2021 Venue: Computers in Biology and Medicine Authors: Luke T. Slater, William Bradlow, Dino FA. Motti, Robert Hoehndorf, Simon Ball, Georgios V. Gkoutos DOI: 10.1016/j.compbiomed.2021.104216 Abstract Semantic similarity is a useful approach for comparing patient phenotypes, and holds the potential of an effective method for exploiting text-derived phenotypes for differential diagnosis, text and document classification, and outcome prediction. While approaches for context disambiguation are commonly used in text mining applications, forming a standard component of information extraction

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 Abstract Ontology-based phenotype profiles have been utilised for the purpose of differential diagnosis of rare genetic diseases, and for decision support in specific disease domains. Particularly, semantic similarity facilitates diagnostic hypothesis generation through comparison with disease phenotype profiles. However, the approach has not been applied for differential diagnosis of common diseases, or generalised

Multi-faceted semantic clustering with text-derived phenotypes

Year: 2021 Venue: Computers in Biology and Medicine Authors: Luke T. Slater, John A. Williams, Andreas Karwath, Hilary Fanning, Simon Ball, Paul N. Schofield, Robert Hoehndorf, Georgios V. Gkoutos DOI: 10.1016/j.compbiomed.2021.104904 Abstract Identification of ontology concepts in clinical narrative text enables the creation of phenotype profiles that can be associated with clinical entities, such as patients or drugs. Constructing patient phenotype profiles using formal ontologies enables their analysis via semantic similarity, in turn enabling the use of background knowledge in clustering

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

Using AberOWL for fast and scalable reasoning over BioPortal ontologies

Year: 2016 Venue: Journal of Biomedical Semantics Authors: Luke Slater, Georgios V. Gkoutos, Paul N. Schofield, Robert Hoehndorf DOI: 10.1186/s13326-016-0090-0 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 provided.

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

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 Acknowledged projects ccf-microbial-cell-factories crg-bio2vec

OPA2Vec: combining formal and informal content of biomedical ontologies to improve similarity-based prediction

Year: 2018 Venue: Bioinformatics Authors: Fatima Zohra Smaili, Xin Gao, Robert Hoehndorf DOI: 10.1093/bioinformatics/bty933 Abstract Motivation: Ontologies are widely used in biology for data annotation, integration and analysis. In addition to formally structured axioms, ontologies contain meta-data in the form of annotation axioms which provide valuable pieces of information that characterize ontology classes. Annotation axioms commonly used in ontologies include class labels, descriptions or synonyms. Despite being a rich source of semantic information, the ontology meta-data are generally

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

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

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

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

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

Similarity-based search of model organism, disease and drug effect phenotypes

Year: 2015 Venue: Journal of Biomedical Semantics Authors: Robert Hoehndorf, Michael Gruenberger, Georgios Gkoutos, Paul Schofield DOI: 10.1186/s13326-015-0001-9 Abstract BACKGROUND:Semantic similarity measures over phenotype ontologies have been demonstrated to provide a powerful approach for the analysis of model organism phenotypes, the discovery of animal models of human disease, novel pathways, gene functions, druggable therapeutic targets, and determination of pathogenicity.RESULTS:We have developed PhenomeNET 2, a system that enables similarity-based searches over a large repository of

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

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

Ontology Embedding: A Survey of Methods, Applications and Resources

Year: 2025 Venue: IEEE Transactions on Knowledge and Data Engineering Authors: Jiaoyan Chen, Olga Mashkova, Fernando Zhapa-Camacho, Robert Hoehndorf, Yuan He, Ian Horrocks DOI: 10.1109/tkde.2025.3559023 Abstract Ontologies are widely used for representing domain knowledge and meta data, playing an increasingly important role in Information Systems, the Semantic Web, Bioinformatics and many other domains. However, logical reasoning that ontologies can directly support are quite limited in learning, approximation and prediction. One straightforward solution is to integrate statistical analysis

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

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. Acknowledged

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

A Machine Learning Based Approach for Similarity Search on Biodiversity Knowledge Graphs

Year: 2019 Venue: Biodiversity Information Science and Standards Authors: Claus Weiland, Maxat Kulmanov, Marco Schmidt, Robert Hoehndorf Abstract Mass biodiversity data from scientific collections will be provided by world-wide digitization efforts like iDigBio in the U.S and DiSSCo in Europe. This opens up an increasing amount of data on wild type organisms, which enables the building of large biodiversity knowledge graphs comprising, inter alia, sequence, trait and occurrence data. Knowledge graphs model information in the form of entities and their relationships expressed in good practice

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

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 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

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 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

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 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

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