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Mullen KR, Tammen I, Matentzoglu NA, Mather M, Mungall CJ, Haendel MA, Nicholas FW, Toro S. The Vertebrate Breed Ontology: Towards Effective Breed Data Standardization. ARXIV 2024:arXiv:2406.02623v1. [PMID: 38883236 PMCID: PMC11177956] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/18/2024]
Abstract
Background – Limited universally adopted data standards in veterinary science hinders data interoperability and therefore integration and comparison; this ultimately impedes application of existing information-based tools to support advancement in veterinary diagnostics, treatments, and precision medicine. Hypothesis/Objectives – Creation of a Vertebrate Breed Ontology (VBO) as a single, coherent logic-based standard for documenting breed names in animal health, production and research-related records will improve data use capabilities in veterinary and comparative medicine. Animals – No live animals were used in this study. Methods – A list of breed names and related information was compiled from relevant sources, organizations, communities, and experts using manual and computational approaches to create VBO. Each breed is represented by a VBO term that includes all provenance and the breed's related information as metadata. VBO terms are classified using description logic to allow computational applications and Artificial Intelligence-readiness. Results – VBO is an open, community-driven ontology representing over 19,000 livestock and companion animal breeds covering 41 species. Breeds are classified based on community and expert conventions (e.g., horse breed, cattle breed). This classification is supported by relations to the breeds' genus and species indicated by NCBI Taxonomy terms. Relationships between VBO terms, e.g. relating breeds to their foundation stock, provide additional context to support advanced data analytics. VBO term metadata includes common names and synonyms, breed identifiers/codes, and attributed cross-references to other databases. Conclusion and clinical importance – Veterinary data interoperability and computability can be enhanced by the adoption of VBO as a source of standard breed names in databases and veterinary electronic health records.
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Schenk PM, Wright AJ, West R, Hastings J, Lorencatto F, Moore C, Hayes E, Schneider V, Howes E, Michie S. An ontology of mechanisms of action in behaviour change interventions. Wellcome Open Res 2024; 8:337. [PMID: 38481854 PMCID: PMC10933577 DOI: 10.12688/wellcomeopenres.19489.1] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/24/2024] [Indexed: 06/04/2024] Open
Abstract
Background Behaviour change interventions influence behaviour through causal processes called "mechanisms of action" (MoAs). Reports of such interventions and their evaluations often use inconsistent or ambiguous terminology, creating problems for searching, evidence synthesis and theory development. This inconsistency includes the reporting of MoAs. An ontology can help address these challenges by serving as a classification system that labels and defines MoAs and their relationships. The aim of this study was to develop an ontology of MoAs of behaviour change interventions. Methods To develop the MoA Ontology, we (1) defined the ontology's scope; (2) identified, labelled and defined the ontology's entities; (3) refined the ontology by annotating (i.e., coding) MoAs in intervention reports; (4) refined the ontology via stakeholder review of the ontology's comprehensiveness and clarity; (5) tested whether researchers could reliably apply the ontology to annotate MoAs in intervention evaluation reports; (6) refined the relationships between entities; (7) reviewed the alignment of the MoA Ontology with other relevant ontologies, (8) reviewed the ontology's alignment with the Theories and Techniques Tool; and (9) published a machine-readable version of the ontology. Results An MoA was defined as "a process that is causally active in the relationship between a behaviour change intervention scenario and its outcome behaviour". We created an initial MoA Ontology with 261 entities through Steps 2-5. Inter-rater reliability for annotating study reports using these entities was α=0.68 ("acceptable") for researchers familiar with the ontology and α=0.47 for researchers unfamiliar with it. As a result of additional revisions (Steps 6-8), 23 further entities were added to the ontology resulting in 284 entities organised in seven hierarchical levels. Conclusions The MoA Ontology extensively captures MoAs of behaviour change interventions. The ontology can serve as a controlled vocabulary for MoAs to consistently describe and synthesise evidence about MoAs across diverse sources.
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Affiliation(s)
- Paulina M Schenk
- Centre for Behaviour Change, University College London, London, England, UK
| | - Alison J Wright
- Centre for Behaviour Change, University College London, London, England, UK
- Institute of Pharmaceutical Science, King's College London, London, England, UK
| | - Robert West
- Department of Behavioural Science and Health, University College London, London, England, UK
| | - Janna Hastings
- Institute for Implementation Science in Health Care, Universitat Zurich, Zürich, Zurich, Switzerland
- School of Medicine, University of St Gallen, St. Gallen, St. Gallen, Switzerland
| | - Fabiana Lorencatto
- Centre for Behaviour Change, University College London, London, England, UK
| | - Candice Moore
- Centre for Behaviour Change, University College London, London, England, UK
| | - Emily Hayes
- Centre for Behaviour Change, University College London, London, England, UK
| | - Verena Schneider
- Research Department of Epidemiology and Public Health, University College London, London, England, UK
| | - Ella Howes
- Leeds Unit for Complex Intervention Development, University of Leeds, Leeds, England, UK
| | - Susan Michie
- Centre for Behaviour Change, University College London, London, England, UK
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Darnala B, Amardeilh F, Roussey C, Todorov K, Jonquet C. C3PO: a crop planning and production process ontology and knowledge graph. Front Artif Intell 2023; 6:1187090. [PMID: 37908741 PMCID: PMC10613657 DOI: 10.3389/frai.2023.1187090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Accepted: 08/28/2023] [Indexed: 11/02/2023] Open
Abstract
Vegetable crop farmers diversify their production by growing a range of crops during the season on the same plot. Crop diversification and rotation enables farmers to increase their income and crop yields while enhancing their farm sustainability against climatic events and pest attacks. Farmers must plan their agricultural work per year and over successive years. Planning decisions are made on the basis of their experience regarding previous plans. For the purpose of assisting farmers in planning decisions and monitoring, we developed the Crop Planning and Production Process Ontology (C3PO), i.e., a representation of agricultural knowledge and data for diversified crop production. C3PO is composed of eight modules to capture all crop production dimensions and complexity for representing farming practices and constraints. It encodes agricultural processes and farm plot organization and captures common agricultural knowledge. C3PO introduces a representation of technical itineraries, i.e., sequences of technical farming tasks to grow vegetables, from soil identification and seed selection to harvest and storage. C3PO is the backbone of a knowledge graph which aggregates data from heterogeneous related semantic resources, e.g., organism taxonomies, chemicals, reference crop listings, or development stages. C3PO and its knowledge graph are used by the Elzeard enterprise to develop knowledge-based decision support systems for farmers. This article describes how we built C3PO and its knowledge graph-which are both publicly available-and briefly outlines their applications.
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Affiliation(s)
- Baptiste Darnala
- LIRMM, CNRS, University of Montpellier, Montpellier, France
- Elzeard, Bordeaux, France
| | | | - Catherine Roussey
- MISTEA, INRAE, Institut Agro, University of Montpellier, Montpellier, France
| | | | - Clément Jonquet
- LIRMM, CNRS, University of Montpellier, Montpellier, France
- MISTEA, INRAE, Institut Agro, University of Montpellier, Montpellier, France
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4
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Stefancsik R, Balhoff JP, Balk MA, Ball RL, Bello SM, Caron AR, Chesler EJ, de Souza V, Gehrke S, Haendel M, Harris LW, Harris NL, Ibrahim A, Koehler S, Matentzoglu N, McMurry JA, Mungall CJ, Munoz-Torres MC, Putman T, Robinson P, Smedley D, Sollis E, Thessen AE, Vasilevsky N, Walton DO, Osumi-Sutherland D. The Ontology of Biological Attributes (OBA)-computational traits for the life sciences. Mamm Genome 2023; 34:364-378. [PMID: 37076585 PMCID: PMC10382347 DOI: 10.1007/s00335-023-09992-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Accepted: 04/06/2023] [Indexed: 04/21/2023]
Abstract
Existing phenotype ontologies were originally developed to represent phenotypes that manifest as a character state in relation to a wild-type or other reference. However, these do not include the phenotypic trait or attribute categories required for the annotation of genome-wide association studies (GWAS), Quantitative Trait Loci (QTL) mappings or any population-focussed measurable trait data. The integration of trait and biological attribute information with an ever increasing body of chemical, environmental and biological data greatly facilitates computational analyses and it is also highly relevant to biomedical and clinical applications. The Ontology of Biological Attributes (OBA) is a formalised, species-independent collection of interoperable phenotypic trait categories that is intended to fulfil a data integration role. OBA is a standardised representational framework for observable attributes that are characteristics of biological entities, organisms, or parts of organisms. OBA has a modular design which provides several benefits for users and data integrators, including an automated and meaningful classification of trait terms computed on the basis of logical inferences drawn from domain-specific ontologies for cells, anatomical and other relevant entities. The logical axioms in OBA also provide a previously missing bridge that can computationally link Mendelian phenotypes with GWAS and quantitative traits. The term components in OBA provide semantic links and enable knowledge and data integration across specialised research community boundaries, thereby breaking silos.
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Affiliation(s)
- Ray Stefancsik
- European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridgeshire, CB10 1SD, UK.
| | - James P Balhoff
- Renaissance Computing Institute, University of North Carolina, Chapel Hill, NC, 27517, USA
| | - Meghan A Balk
- Natural History Museum, University of Oslo, Oslo, Norway
| | - Robyn L Ball
- The Jackson Laboratory, Bar Harbor, ME, 04609, USA
| | | | - Anita R Caron
- European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridgeshire, CB10 1SD, UK
| | | | - Vinicius de Souza
- European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridgeshire, CB10 1SD, UK
| | - Sarah Gehrke
- Anschutz Medical Campus, University of Colorado, Aurora, CO, 80045, USA
| | - Melissa Haendel
- Anschutz Medical Campus, University of Colorado, Aurora, CO, 80045, USA
| | - Laura W Harris
- European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridgeshire, CB10 1SD, UK
| | - Nomi L Harris
- Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Arwa Ibrahim
- European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridgeshire, CB10 1SD, UK
| | | | | | - Julie A McMurry
- Anschutz Medical Campus, University of Colorado, Aurora, CO, 80045, USA
| | - Christopher J Mungall
- Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | | | - Tim Putman
- Anschutz Medical Campus, University of Colorado, Aurora, CO, 80045, USA
| | | | - Damian Smedley
- William Harvey Research Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, EC1M 6BQ, UK
| | - Elliot Sollis
- European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridgeshire, CB10 1SD, UK
| | - Anne E Thessen
- Anschutz Medical Campus, University of Colorado, Aurora, CO, 80045, USA
| | - Nicole Vasilevsky
- Data Collaboration Center, Critical Path Institute, Tucson, AZ, 85718, USA
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Kurteva A, Chhetri TR, Tauqeer A, Hilscher R, Fensel A, Nagorny K, Correia A, Zilverberg A, Schestakov S, Funke T, Demidova E. The smashHitCore Ontology for GDPR-Compliant Sensor Data Sharing in Smart Cities. SENSORS (BASEL, SWITZERLAND) 2023; 23:6188. [PMID: 37448036 PMCID: PMC10346455 DOI: 10.3390/s23136188] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 06/21/2023] [Accepted: 07/03/2023] [Indexed: 07/15/2023]
Abstract
The adoption of the General Data Protection Regulation (GDPR) has resulted in a significant shift in how the data of European Union citizens is handled. A variety of data sharing challenges in scenarios such as smart cities have arisen, especially when attempting to semantically represent GDPR legal bases, such as consent, contracts and the data types and specific sources related to them. Most of the existing ontologies that model GDPR focus mainly on consent. In order to represent other GDPR bases, such as contracts, multiple ontologies need to be simultaneously reused and combined, which can result in inconsistent and conflicting knowledge representation. To address this challenge, we present the smashHitCore ontology. smashHitCore provides a unified and coherent model for both consent and contracts, as well as the sensor data and data processing associated with them. The ontology was developed in response to real-world sensor data sharing use cases in the insurance and smart city domains. The ontology has been successfully utilised to enable GDPR-complaint data sharing in a connected car for insurance use cases and in a city feedback system as part of a smart city use case.
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Affiliation(s)
- Anelia Kurteva
- Semantic Technology Institute (STI), Department of Computer Science, Universität Innsbruck, 6020 Innsbruck, Austria; (A.K.); (T.R.C.); or (A.T.); (R.H.)
- Industrial Design Engineering, Delft University of Technology, 2628 CE Delft, The Netherlands
| | - Tek Raj Chhetri
- Semantic Technology Institute (STI), Department of Computer Science, Universität Innsbruck, 6020 Innsbruck, Austria; (A.K.); (T.R.C.); or (A.T.); (R.H.)
| | - Amar Tauqeer
- Semantic Technology Institute (STI), Department of Computer Science, Universität Innsbruck, 6020 Innsbruck, Austria; (A.K.); (T.R.C.); or (A.T.); (R.H.)
- Consumption and Healthy Lifestyles Chair Group, Wageningen University & Research, 6706 KN Wageningen, The Netherlands
| | - Rainer Hilscher
- Semantic Technology Institute (STI), Department of Computer Science, Universität Innsbruck, 6020 Innsbruck, Austria; (A.K.); (T.R.C.); or (A.T.); (R.H.)
- RTI International, Research Triangle Park, NC 27709, USA
| | - Anna Fensel
- Semantic Technology Institute (STI), Department of Computer Science, Universität Innsbruck, 6020 Innsbruck, Austria; (A.K.); (T.R.C.); or (A.T.); (R.H.)
- Consumption and Healthy Lifestyles Chair Group, Wageningen University & Research, 6706 KN Wageningen, The Netherlands
- Wageningen Data Competence Center, Wageningen University & Research, 6708 PB Wageningen, The Netherlands
| | - Kevin Nagorny
- Institut für Angewandte Systemtechnik Bremen GmbH (ATB), 28359 Bremen, Germany; (K.N.); (A.C.); (A.Z.)
| | - Ana Correia
- Institut für Angewandte Systemtechnik Bremen GmbH (ATB), 28359 Bremen, Germany; (K.N.); (A.C.); (A.Z.)
| | - Albert Zilverberg
- Institut für Angewandte Systemtechnik Bremen GmbH (ATB), 28359 Bremen, Germany; (K.N.); (A.C.); (A.Z.)
| | - Stefan Schestakov
- L3S Research Center, Leibniz University Hannover, 30167 Hannover, Germany; (S.S.); (T.F.)
| | - Thorben Funke
- L3S Research Center, Leibniz University Hannover, 30167 Hannover, Germany; (S.S.); (T.F.)
| | - Elena Demidova
- Data Science and Intelligent Systems Group (DSIS), University of Bonn, 53115 Bonn, Germany;
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6
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Chan LE, Thessen AE, Duncan WD, Matentzoglu N, Schmitt C, Grondin CJ, Vasilevsky N, McMurry JA, Robinson PN, Mungall CJ, Haendel MA. The Environmental Conditions, Treatments, and Exposures Ontology (ECTO): connecting toxicology and exposure to human health and beyond. J Biomed Semantics 2023; 14:3. [PMID: 36823605 PMCID: PMC9951428 DOI: 10.1186/s13326-023-00283-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Accepted: 02/03/2023] [Indexed: 02/25/2023] Open
Abstract
BACKGROUND Evaluating the impact of environmental exposures on organism health is a key goal of modern biomedicine and is critically important in an age of greater pollution and chemicals in our environment. Environmental health utilizes many different research methods and generates a variety of data types. However, to date, no comprehensive database represents the full spectrum of environmental health data. Due to a lack of interoperability between databases, tools for integrating these resources are needed. In this manuscript we present the Environmental Conditions, Treatments, and Exposures Ontology (ECTO), a species-agnostic ontology focused on exposure events that occur as a result of natural and experimental processes, such as diet, work, or research activities. ECTO is intended for use in harmonizing environmental health data resources to support cross-study integration and inference for mechanism discovery. METHODS AND FINDINGS ECTO is an ontology designed for describing organismal exposures such as toxicological research, environmental variables, dietary features, and patient-reported data from surveys. ECTO utilizes the base model established within the Exposure Ontology (ExO). ECTO is developed using a combination of manual curation and Dead Simple OWL Design Patterns (DOSDP), and contains over 2700 environmental exposure terms, and incorporates chemical and environmental ontologies. ECTO is an Open Biological and Biomedical Ontology (OBO) Foundry ontology that is designed for interoperability, reuse, and axiomatization with other ontologies. ECTO terms have been utilized in axioms within the Mondo Disease Ontology to represent diseases caused or influenced by environmental factors, as well as for survey encoding for the Personalized Environment and Genes Study (PEGS). CONCLUSIONS We constructed ECTO to meet Open Biological and Biomedical Ontology (OBO) Foundry principles to increase translation opportunities between environmental health and other areas of biology. ECTO has a growing community of contributors consisting of toxicologists, public health epidemiologists, and health care providers to provide the necessary expertise for areas that have been identified previously as gaps.
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Affiliation(s)
- Lauren E. Chan
- grid.4391.f0000 0001 2112 1969Oregon State University, Corvallis, OR 97331 USA
| | - Anne E. Thessen
- grid.4391.f0000 0001 2112 1969Oregon State University, Corvallis, OR 97331 USA ,grid.430503.10000 0001 0703 675XUniversity of Colorado Anschutz Medical Campus, Aurora, CO 80054 USA
| | - William D. Duncan
- grid.15276.370000 0004 1936 8091University of Florida, Gainesville, FL 32610 USA
| | | | - Charles Schmitt
- grid.280664.e0000 0001 2110 5790National Institute of Environmental Health Sciences, Durham, NC 27709 USA
| | - Cynthia J. Grondin
- grid.40803.3f0000 0001 2173 6074North Carolina State University, Raleigh, NC 27965 USA
| | - Nicole Vasilevsky
- grid.430503.10000 0001 0703 675XUniversity of Colorado Anschutz Medical Campus, Aurora, CO 80054 USA
| | - Julie A. McMurry
- grid.430503.10000 0001 0703 675XUniversity of Colorado Anschutz Medical Campus, Aurora, CO 80054 USA
| | - Peter N. Robinson
- grid.249880.f0000 0004 0374 0039The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032 USA
| | - Christopher J. Mungall
- grid.184769.50000 0001 2231 4551Lawrence Berkeley National Laboratory, Berkeley, CA 94720 USA
| | - Melissa A. Haendel
- grid.4391.f0000 0001 2112 1969Oregon State University, Corvallis, OR 97331 USA ,grid.430503.10000 0001 0703 675XUniversity of Colorado Anschutz Medical Campus, Aurora, CO 80054 USA
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7
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Stefancsik R, Balhoff JP, Balk MA, Ball R, Bello SM, Caron AR, Chessler E, de Souza V, Gehrke S, Haendel M, Harris LW, Harris NL, Ibrahim A, Koehler S, Matentzoglu N, McMurry JA, Mungall CJ, Munoz-Torres MC, Putman T, Robinson P, Smedley D, Sollis E, Thessen AE, Vasilevsky N, Walton DO, Osumi-Sutherland D. The Ontology of Biological Attributes (OBA) - Computational Traits for the Life Sciences. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.26.525742. [PMID: 36747660 PMCID: PMC9900877 DOI: 10.1101/2023.01.26.525742] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
Abstract
Existing phenotype ontologies were originally developed to represent phenotypes that manifest as a character state in relation to a wild-type or other reference. However, these do not include the phenotypic trait or attribute categories required for the annotation of genome-wide association studies (GWAS), Quantitative Trait Loci (QTL) mappings or any population-focused measurable trait data. Moreover, variations in gene expression in response to environmental disturbances even without any genetic alterations can also be associated with particular biological attributes. The integration of trait and biological attribute information with an ever increasing body of chemical, environmental and biological data greatly facilitates computational analyses and it is also highly relevant to biomedical and clinical applications. The Ontology of Biological Attributes (OBA) is a formalised, species-independent collection of interoperable phenotypic trait categories that is intended to fulfil a data integration role. OBA is a standardised representational framework for observable attributes that are characteristics of biological entities, organisms, or parts of organisms. OBA has a modular design which provides several benefits for users and data integrators, including an automated and meaningful classification of trait terms computed on the basis of logical inferences drawn from domain-specific ontologies for cells, anatomical and other relevant entities. The logical axioms in OBA also provide a previously missing bridge that can computationally link Mendelian phenotypes with GWAS and quantitative traits. The term components in OBA provide semantic links and enable knowledge and data integration across specialised research community boundaries, thereby breaking silos.
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Affiliation(s)
- Ray Stefancsik
- European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridgeshire, CB10 1SD, UK
| | - James P. Balhoff
- Renaissance Computing Institute, University of North Carolina, Chapel Hill, NC 27517, USA
| | - Meghan A. Balk
- National Ecological Observatory Network, Battelle, Boulder, CO 80301, USA
| | - Robyn Ball
- The Jackson Laboratory, Bar Harbor, ME 04609, USA
| | | | - Anita R. Caron
- European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridgeshire, CB10 1SD, UK
| | | | - Vinicius de Souza
- European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridgeshire, CB10 1SD, UK
| | - Sarah Gehrke
- Anschutz Medical Campus, University of Colorado, Aurora, CO 80045, USA
| | - Melissa Haendel
- Anschutz Medical Campus, University of Colorado, Aurora, CO 80045, USA
| | - Laura W. Harris
- European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridgeshire, CB10 1SD, UK
| | - Nomi L. Harris
- Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Arwa Ibrahim
- European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridgeshire, CB10 1SD, UK
| | | | | | - Julie A. McMurry
- Anschutz Medical Campus, University of Colorado, Aurora, CO 80045, USA
| | - Christopher J. Mungall
- Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | | | - Tim Putman
- Anschutz Medical Campus, University of Colorado, Aurora, CO 80045, USA
| | | | - Damian Smedley
- William Harvey Research Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London EC1M 6BQ, UK
| | - Elliot Sollis
- European Bioinformatics Institute (EMBL-EBI), Hinxton, Cambridgeshire, CB10 1SD, UK
| | - Anne E Thessen
- Anschutz Medical Campus, University of Colorado, Aurora, CO 80045, USA
| | - Nicole Vasilevsky
- Anschutz Medical Campus, University of Colorado, Aurora, CO 80045, USA
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The Representation of Causality and Causation with Ontologies: A Systematic Literature Review. Online J Public Health Inform 2022; 14:e4. [PMID: 36120162 PMCID: PMC9473331 DOI: 10.5210/ojphi.v14i1.12577] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022] Open
Abstract
Objective To explore how disease-related causality is formally represented in current ontologies and identify their potential limitations. Methods We conducted a systematic literature search on eight databases (PubMed, Institute of Electrical and Electronic Engendering (IEEE Xplore), Association for Computing Machinery (ACM), Scopus, Web of Science databases, Ontobee, OBO Foundry, and Bioportal. We included studies published between January 1, 1970, and December 9, 2020, that formally represent the notions of causality and causation in the medical domain using ontology as a representational tool. Further inclusion criteria were publication in English and peer-reviewed journals or conference proceedings. Two authors (SS, RM) independently assessed study quality and performed content analysis using a modified validated extraction grid with pre-established categorization. Results The search strategy led to a total of 8,501 potentially relevant papers, of which 50 met the inclusion criteria. Only 14 out of 50 (28%) specified the nature of causation, and only 7 (14%) included clear and non-circular natural language definitions. Although several theories of causality were mentioned, none of the articles offers a widely accepted conceptualization of how causation and causality can be formally represented. Conclusion No current ontology captures the wealth of available concepts of causality. This provides an opportunity for the development of a formal ontology of causation/causality.
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Ishaq M, Khan A, Asim M, Khan A, Iqbal Bangash J. Comprehensive selective improvements in agri-informatics semantics. J Inf Sci 2022. [DOI: 10.1177/01655515221110987] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
The advent of information technology re-innovates all sectors of bio-sciences. Researchers use Semantic Web to improve web searching, mining and integration, which alleviates the time-consuming task of finding relevant and high-quality content. Semantics is improved through ontology engineering in any domain. Amended and developed ontologies will be uploaded to existing standardised and approved biomedical repositories. The establishment of a World Wide Web Consortium (W3C) approved and standardised ontology repository is the most ambitious goal. This work will solely focus on some selected agri-ontologies. The main objective is to promote outcome-based research and transformation styles of relevant expertise sharing. The intended goal is to win project funding to train and equip students with relevant skills and expertise. Need-based and market-oriented training and professional grooming are a tangible asset for students. The majority of traditional Web development freelancers are unaware of ontology or semantic web market demand. Freelancing is another option for expert Ontology developers. However, agriculture students are used to all the research vocabulary and terminologies in their area, but they do not know how to contribute their expertise to improve the efficiency of the Semantic Web in their domain. If the improvement in relevant ontology becomes a part of the Semantic Web, then it is termed ‘Real-time Web semantics enhancement’. In other words, the target ontology becomes a part of the future Web of meaning.
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Affiliation(s)
- Muhammad Ishaq
- Institute of Computer Sciences and Information Technology, The University of Agriculture, Peshawar, Pakistan
| | - Abdullah Khan
- Institute of Computer Sciences and Information Technology, The University of Agriculture, Peshawar, Pakistan
| | - Muhammad Asim
- Institute of Computer Sciences and Information Technology, The University of Agriculture, Peshawar, Pakistan
| | - Asfandyar Khan
- Institute of Computer Sciences and Information Technology, The University of Agriculture, Peshawar, Pakistan
| | - Javed Iqbal Bangash
- Institute of Computer Sciences and Information Technology, The University of Agriculture, Peshawar, Pakistan
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Fisher ME, Segerdell E, Matentzoglu N, Nenni MJ, Fortriede JD, Chu S, Pells TJ, Osumi-Sutherland D, Chaturvedi P, James-Zorn C, Sundararaj N, Lotay VS, Ponferrada V, Wang DZ, Kim E, Agalakov S, Arshinoff BI, Karimi K, Vize PD, Zorn AM. The Xenopus phenotype ontology: bridging model organism phenotype data to human health and development. BMC Bioinformatics 2022; 23:99. [PMID: 35317743 PMCID: PMC8939077 DOI: 10.1186/s12859-022-04636-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Accepted: 03/08/2022] [Indexed: 11/10/2022] Open
Abstract
Background Ontologies of precisely defined, controlled vocabularies are essential to curate the results of biological experiments such that the data are machine searchable, can be computationally analyzed, and are interoperable across the biomedical research continuum. There is also an increasing need for methods to interrelate phenotypic data easily and accurately from experiments in animal models with human development and disease. Results Here we present the Xenopus phenotype ontology (XPO) to annotate phenotypic data from experiments in Xenopus, one of the major vertebrate model organisms used to study gene function in development and disease. The XPO implements design patterns from the Unified Phenotype Ontology (uPheno), and the principles outlined by the Open Biological and Biomedical Ontologies (OBO Foundry) to maximize interoperability with other species and facilitate ongoing ontology management. Constructed in Web Ontology Language (OWL) the XPO combines the existing uPheno library of ontology design patterns with additional terms from the Xenopus Anatomy Ontology (XAO), the Phenotype and Trait Ontology (PATO) and the Gene Ontology (GO). The integration of these different ontologies into the XPO enables rich phenotypic curation, whilst the uPheno bridging axioms allows phenotypic data from Xenopus experiments to be related to phenotype data from other model organisms and human disease. Moreover, the simple post-composed uPheno design patterns facilitate ongoing XPO development as the generation of new terms and classes of terms can be substantially automated. Conclusions The XPO serves as an example of current best practices to help overcome many of the inherent challenges in harmonizing phenotype data between different species. The XPO currently consists of approximately 22,000 terms and is being used to curate phenotypes by Xenbase, the Xenopus Model Organism Knowledgebase, forming a standardized corpus of genotype–phenotype data that can be directly related to other uPheno compliant resources. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-022-04636-8.
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Affiliation(s)
- Malcolm E Fisher
- Division of Developmental Biology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Erik Segerdell
- Division of Developmental Biology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Nicolas Matentzoglu
- Monarch Initiative, London, UK.,Semanticly Ltd, London, UK.,European Bioinformatics Institute (EMBL-EBI), Hinxton, UK
| | - Mardi J Nenni
- Division of Developmental Biology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Joshua D Fortriede
- Division of Developmental Biology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Stanley Chu
- Department of Biological Science, University of Calgary, Calgary, AB, Canada
| | - Troy J Pells
- Department of Biological Science, University of Calgary, Calgary, AB, Canada
| | | | - Praneet Chaturvedi
- Division of Developmental Biology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Christina James-Zorn
- Division of Developmental Biology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Nivitha Sundararaj
- Division of Developmental Biology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Vaneet S Lotay
- Department of Biological Science, University of Calgary, Calgary, AB, Canada
| | - Virgilio Ponferrada
- Division of Developmental Biology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Dong Zhuo Wang
- Department of Biological Science, University of Calgary, Calgary, AB, Canada
| | - Eugene Kim
- Department of Biological Science, University of Calgary, Calgary, AB, Canada
| | - Sergei Agalakov
- Department of Biological Science, University of Calgary, Calgary, AB, Canada
| | - Bradley I Arshinoff
- Department of Biological Science, University of Calgary, Calgary, AB, Canada
| | - Kamran Karimi
- Department of Biological Science, University of Calgary, Calgary, AB, Canada
| | - Peter D Vize
- Department of Biological Science, University of Calgary, Calgary, AB, Canada
| | - Aaron M Zorn
- Division of Developmental Biology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA.
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11
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Slater LT, Russell S, Makepeace S, Carberry A, Karwath A, Williams JA, Fanning H, Ball S, Hoehndorf R, Gkoutos GV. Evaluating semantic similarity methods for comparison of text-derived phenotype profiles. BMC Med Inform Decis Mak 2022; 22:33. [PMID: 35123470 PMCID: PMC8818208 DOI: 10.1186/s12911-022-01770-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Accepted: 01/21/2022] [Indexed: 11/16/2022] Open
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 exists exploring the use of semantic similarity for multiple tasks, including protein interaction prediction, and rare disease differential diagnosis, there is less work exploring comparison of patient phenotype profiles for clinical tasks. Moreover, there are no experimental explorations of optimal parameters or better methods in the area. Methods We develop a platform for reproducible benchmarking and comparison of experimental conditions for patient phentoype similarity. Using the platform, we evaluate the task of ranking shared primary diagnosis from uncurated phenotype profiles derived from all text narrative associated with admissions in the medical information mart for intensive care (MIMIC-III). Results 300 semantic similarity configurations were evaluated, as well as one embedding-based approach. On average, measures that did not make use of an external information content measure performed slightly better, however the best-performing configurations when measured by area under receiver operating characteristic curve and Top Ten Accuracy used term-specificity and annotation-frequency measures. Conclusion We identified and interpreted the performance of a large number of semantic similarity configurations for the task of classifying diagnosis from text-derived phenotype profiles in one setting. We also provided a basis for further research on other settings and related tasks in the area.
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12
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Liu M, Liu J, Liu G, Wang H, Wang X, Deng Z, He Y, Ou HY. ICEO, a biological ontology for representing and analyzing bacterial integrative and conjugative elements. Sci Data 2022; 9:11. [PMID: 35058462 PMCID: PMC8776819 DOI: 10.1038/s41597-021-01112-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Accepted: 12/13/2021] [Indexed: 01/18/2023] Open
Abstract
Bacterial integrative and conjugative elements (ICEs) are highly modular mobile genetic elements critical to the horizontal transfer of antibiotic resistance and virulence factor genes. To better understand and analyze the ongoing increase of ICEs, we developed an Integrative and Conjugative Element Ontology (ICEO) to represent the gene components, functional modules, and other information of experimentally verified ICEs. ICEO is aligned with the upper-level Basic Formal Ontology and reuses existing reliable ontologies. There are 31,081 terms, including 26,814 classes from 14 ontologies and 4128 ICEO-specific classes, representing the information of 271 known experimentally verified ICEs from 235 bacterial strains in ICEO currently and 311 predicted ICEs of 272 completely sequenced Klebsiella pneumoniae strains. Three ICEO use cases were illustrated to investigate complex joins of ICEs and their harboring antibiotic resistance or virulence factor genes by using SPARQL or DL query. ICEO has been approved as an Open Biomedical Ontology library ontology. It may be dedicated to facilitating systematical ICE knowledge representation, integration, and computer-assisted queries.
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Affiliation(s)
- Meng Liu
- State Key Laboratory of Microbial Metabolism, Joint International Laboratory on Metabolic & Developmental Sciences, School of Life Sciences & Biotechnology, Shanghai Jiao Tong University, Shanghai, 200030, China
| | - Jialin Liu
- Department of Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Guitian Liu
- State Key Laboratory of Microbial Metabolism, Joint International Laboratory on Metabolic & Developmental Sciences, School of Life Sciences & Biotechnology, Shanghai Jiao Tong University, Shanghai, 200030, China
| | - Hui Wang
- State Key Laboratory of Pathogens and Biosecurity, Beijing Institute of Microbiology and Epidemiology, Beijing, 100071, China
| | - Xiaoli Wang
- Department of Critical Care Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Zixin Deng
- State Key Laboratory of Microbial Metabolism, Joint International Laboratory on Metabolic & Developmental Sciences, School of Life Sciences & Biotechnology, Shanghai Jiao Tong University, Shanghai, 200030, China
| | - Yongqun He
- University of Michigan Medical School, Ann Arbor, MI, 48109, USA.
| | - Hong-Yu Ou
- State Key Laboratory of Microbial Metabolism, Joint International Laboratory on Metabolic & Developmental Sciences, School of Life Sciences & Biotechnology, Shanghai Jiao Tong University, Shanghai, 200030, China.
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13
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Kim H, Jung J, Choi J. Developing a Dietary Lifestyle Ontology (DILON) to Improve the Interoperability of Dietary Data: A Proof-of-Concept Study (Preprint). JMIR Form Res 2021; 6:e34962. [PMID: 35451991 PMCID: PMC9073603 DOI: 10.2196/34962] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Revised: 03/12/2022] [Accepted: 03/16/2022] [Indexed: 11/13/2022] Open
Abstract
Background Dietary habits offer crucial information on one's health and form a considerable part of the patient-generated health data. Dietary data are collected through various channels and formats; thus, interoperability is a significant challenge to reusing this type of data. The vast scope of dietary concepts and the colloquial expression style add difficulty to standardizing the data. The interoperability issues of dietary data can be addressed through Common Data Elements with metadata annotation to some extent. However, making culture-specific dietary habits and questionnaire-based dietary assessment data interoperable still requires substantial efforts. Objective The main goal of this study was to address the interoperability challenge of questionnaire-based dietary data from different cultural backgrounds by combining ontological curation and metadata annotation of dietary concepts. Specifically, this study aimed to develop a Dietary Lifestyle Ontology (DILON) and demonstrate the improved interoperability of questionnaire-based dietary data by annotating its main semantics with DILON. Methods By analyzing 1158 dietary assessment data elements (367 in Korean and 791 in English), 515 dietary concepts were extracted and used to construct DILON. To demonstrate the utility of DILON in addressing the interoperability challenges of questionnaire-based multicultural dietary data, we developed 10 competency questions that asked to identify data elements sharing the same dietary topics and assessment properties. We instantiated 68 data elements on dietary habits selected from Korean and English questionnaires and annotated them with DILON to answer the competency questions. We translated the competency questions into Semantic Query-Enhanced Web Rule Language and reviewed the query results for accuracy. Results DILON was built with 262 concept classes and validated with ontology validation tools. A small overlap (72 concepts) in the concepts extracted from the questionnaires in 2 languages indicates that we need to pay closer attention to representing culture-specific dietary concepts. The Semantic Query-Enhanced Web Rule Language queries reflecting the 10 competency questions yielded correct results. Conclusions Ensuring the interoperability of dietary lifestyle data is a demanding task due to its vast scope and variations in expression. This study demonstrated that we could improve the interoperability of dietary data generated in different cultural contexts and expressed in various styles by annotating their core semantics with DILON.
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Affiliation(s)
- Hyeoneui Kim
- The Research Institute of Nursing Science, College of Nursing, Seoul National University, Seoul, Republic of Korea
| | - Jinsun Jung
- The Research Institute of Nursing Science, College of Nursing, Seoul National University, Seoul, Republic of Korea
| | - Jisung Choi
- Samsung Medical Center, Seoul, Republic of Korea
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14
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Jackson R, Matentzoglu N, Overton JA, Vita R, Balhoff JP, Buttigieg PL, Carbon S, Courtot M, Diehl AD, Dooley DM, Duncan WD, Harris NL, Haendel MA, Lewis SE, Natale DA, Osumi-Sutherland D, Ruttenberg A, Schriml LM, Smith B, Stoeckert CJ, Vasilevsky NA, Walls RL, Zheng J, Mungall CJ, Peters B. OBO Foundry in 2021: operationalizing open data principles to evaluate ontologies. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2021; 2021:6410158. [PMID: 34697637 PMCID: PMC8546234 DOI: 10.1093/database/baab069] [Citation(s) in RCA: 70] [Impact Index Per Article: 23.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Revised: 10/05/2021] [Accepted: 10/13/2021] [Indexed: 11/26/2022]
Abstract
Biological ontologies are used to organize, curate and interpret the vast quantities of data arising from biological experiments. While this works well when using a single ontology, integrating multiple ontologies can be problematic, as they are developed independently, which can lead to incompatibilities. The Open Biological and Biomedical Ontologies (OBO) Foundry was created to address this by facilitating the development, harmonization, application and sharing of ontologies, guided by a set of overarching principles. One challenge in reaching these goals was that the OBO principles were not originally encoded in a precise fashion, and interpretation was subjective. Here, we show how we have addressed this by formally encoding the OBO principles as operational rules and implementing a suite of automated validation checks and a dashboard for objectively evaluating each ontology’s compliance with each principle. This entailed a substantial effort to curate metadata across all ontologies and to coordinate with individual stakeholders. We have applied these checks across the full OBO suite of ontologies, revealing areas where individual ontologies require changes to conform to our principles. Our work demonstrates how a sizable, federated community can be organized and evaluated on objective criteria that help improve overall quality and interoperability, which is vital for the sustenance of the OBO project and towards the overall goals of making data Findable, Accessible, Interoperable, and Reusable (FAIR). Database URL http://obofoundry.org/
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Affiliation(s)
- Rebecca Jackson
- Bend Informatics LLC, 20770 Double Peaks Drive, Bend, OR 97701, USA
| | | | - James A Overton
- Knocean Inc., 2-107 Quebec Ave., Toronto, ON M6P 2T3, Canada
| | - Randi Vita
- La Jolla Institute for Immunology, 9420 Athena Cir, La Jolla, CA 92037, USA
| | - James P Balhoff
- Renaissance Computing Institute, University of North Carolina, 100 Europa Drive, Suite 540, Chapel Hill, NC 27517, USA
| | - Pier Luigi Buttigieg
- Alfred Wegener Institute, Helmholtz Center for Polar and Marine Research, Am Handelshafen 12, Bremerhaven 27570, Germany
| | - Seth Carbon
- Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, 1 Cyclotron Rd., Berkeley, CA 94720, USA
| | - Melanie Courtot
- European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton CB10 1SD, UK
| | - Alexander D Diehl
- Department of Biomedical Informatics, University at Buffalo, 77 Goodell St, Buffalo, NY 14203, USA
| | - Damion M Dooley
- Centre for Infectious Disease Genomics and One Health, Simon Fraser University, 8888 University Dr, Burnaby, BC V5A 1S6, Canada
| | - William D Duncan
- Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, 1 Cyclotron Rd., Berkeley, CA 94720, USA
| | - Nomi L Harris
- Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, 1 Cyclotron Rd., Berkeley, CA 94720, USA
| | - Melissa A Haendel
- Biochemistry and Molecular Genetics Department, University of Colorado School of Medicine, PO Box 6511, Aurora, CO 80045, USA
| | - Suzanna E Lewis
- Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, 1 Cyclotron Rd., Berkeley, CA 94720, USA
| | - Darren A Natale
- Department of Biochemistry and Molecular & Cellular Biology, Georgetown University Medical Center, 2115 Wisconsin Avenue NW, Washington, DC 20007, USA
| | - David Osumi-Sutherland
- European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton CB10 1SD, UK
| | - Alan Ruttenberg
- Department of Biomedical Informatics, University at Buffalo, 77 Goodell St, Buffalo, NY 14203, USA
| | - Lynn M Schriml
- School of Medicine, University of Maryland, 655 W Baltimore St S, Baltimore, MD 21201, USA
| | - Barry Smith
- Department of Biomedical Informatics, University at Buffalo, 77 Goodell St, Buffalo, NY 14203, USA
| | - Christian J Stoeckert
- Department of Genetics and Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, 3400 Civic Center Blvd, Philadelphia, PA 19104, USA
| | - Nicole A Vasilevsky
- Biochemistry and Molecular Genetics Department, University of Colorado School of Medicine, PO Box 6511, Aurora, CO 80045, USA
| | - Ramona L Walls
- Critical Path Institute, 1730 E River Rd #200, Tucson, AZ 85718, USA
| | - Jie Zheng
- Department of Genetics and Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, 3400 Civic Center Blvd, Philadelphia, PA 19104, USA
| | - Christopher J Mungall
- Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, 1 Cyclotron Rd., Berkeley, CA 94720, USA
| | - Bjoern Peters
- La Jolla Institute for Immunology, 9420 Athena Cir, La Jolla, CA 92037, USA
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15
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Abstract
Digital technologies are an opportunity to overcome disabilities, provided that accessibility is ensured. In this paper, we focus on visual accessibility and the way it is supported in Operating Systems (OS). The significant variability in this support has practical consequences, e.g., the difficulty to recommend or select an OS, or migrate from one OS to another. This suggests building a variability model for OS that would classify them and would serve as a reference. We propose a methodology to build such a variability model with the help of the Formal Concept Analysis (FCA) framework. In addition, as visual accessibility can be divided into several concerns (e.g., zoom, or contrast), we leverage an extension of FCA, namely Relational Concept Analysis. We also build an ontology to dispose of a standardized description of visual accessibility options. We apply our proposal to the analysis of the variability of a few representative operating systems.
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16
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Nowotarski SH, Davies EL, Robb SMC, Ross EJ, Matentzoglu N, Doddihal V, Mir M, McClain M, Sánchez Alvarado A. Planarian Anatomy Ontology: a resource to connect data within and across experimental platforms. Development 2021; 148:271068. [PMID: 34318308 PMCID: PMC8353266 DOI: 10.1242/dev.196097] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Accepted: 06/28/2021] [Indexed: 12/23/2022]
Abstract
As the planarian research community expands, the need for an interoperable data organization framework for tool building has become increasingly apparent. Such software would streamline data annotation and enhance cross-platform and cross-species searchability. We created the Planarian Anatomy Ontology (PLANA), an extendable relational framework of defined Schmidtea mediterranea (Smed) anatomical terms used in the field. At publication, PLANA contains over 850 terms describing Smed anatomy from subcellular to system levels across all life cycle stages, in intact animals and regenerating body fragments. Terms from other anatomy ontologies were imported into PLANA to promote interoperability and comparative anatomy studies. To demonstrate the utility of PLANA as a tool for data curation, we created resources for planarian embryogenesis, including a staging series and molecular fate-mapping atlas, and the Planarian Anatomy Gene Expression database, which allows retrieval of a variety of published transcript/gene expression data associated with PLANA terms. As an open-source tool built using FAIR (findable, accessible, interoperable, reproducible) principles, our strategy for continued curation and versioning of PLANA also provides a platform for community-led growth and evolution of this resource. Summary: Description of the construction of an anatomy ontology tool for planaria with examples of its potential use to curate and mine data across multiple experimental platforms.
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Affiliation(s)
- Stephanie H Nowotarski
- Stowers Institute for Medical Research, Kansas City, MO 64110, USA.,Howard Hughes Medical Institute, Chevy Chase, MD 20815, USA
| | - Erin L Davies
- Stowers Institute for Medical Research, Kansas City, MO 64110, USA.,Center for Cancer Research, National Cancer Institute, National Institutes of Health, Frederick, MD 21702, USA
| | - Sofia M C Robb
- Stowers Institute for Medical Research, Kansas City, MO 64110, USA
| | - Eric J Ross
- Stowers Institute for Medical Research, Kansas City, MO 64110, USA.,Howard Hughes Medical Institute, Chevy Chase, MD 20815, USA
| | - Nicolas Matentzoglu
- European Bioinformatics Institute (EMBL-EBI), European Molecular Biology Laboratory, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Viraj Doddihal
- Stowers Institute for Medical Research, Kansas City, MO 64110, USA
| | - Mol Mir
- Stowers Institute for Medical Research, Kansas City, MO 64110, USA
| | - Melainia McClain
- Stowers Institute for Medical Research, Kansas City, MO 64110, USA
| | - Alejandro Sánchez Alvarado
- Stowers Institute for Medical Research, Kansas City, MO 64110, USA.,Howard Hughes Medical Institute, Chevy Chase, MD 20815, USA
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17
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Pendleton SC, Slater K, Karwath A, Gilbert RM, Davis N, Pesudovs K, Liu X, Denniston AK, Gkoutos GV, Braithwaite T. Development and application of the ocular immune-mediated inflammatory diseases ontology enhanced with synonyms from online patient support forum conversation. Comput Biol Med 2021; 135:104542. [PMID: 34139439 PMCID: PMC8404035 DOI: 10.1016/j.compbiomed.2021.104542] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Revised: 05/27/2021] [Accepted: 05/30/2021] [Indexed: 11/28/2022]
Abstract
BACKGROUND Unstructured text created by patients represents a rich, but relatively inaccessible resource for advancing patient-centred care. This study aimed to develop an ontology for ocular immune-mediated inflammatory diseases (OcIMIDo), as a tool to facilitate data extraction and analysis, illustrating its application to online patient support forum data. METHODS We developed OcIMIDo using clinical guidelines, domain expertise, and cross-references to classes from other biomedical ontologies. We developed an approach to add patient-preferred synonyms text-mined from oliviasvision.org online forum, using statistical ranking. We validated the approach with split-sampling and comparison to manual extraction. Using OcIMIDo, we then explored the frequency of OcIMIDo classes and synonyms, and their potential association with natural language sentiment expressed in each online forum post. FINDINGS OcIMIDo (version 1.2) includes 661 classes, describing anatomy, clinical phenotype, disease activity status, complications, investigations, interventions and functional impacts. It contains 1661 relationships and axioms, 2851 annotations, including 1131 database cross-references, and 187 patient-preferred synonyms. To illustrate OcIMIDo's potential applications, we explored 9031 forum posts, revealing frequent mention of different clinical phenotypes, treatments, and complications. Language sentiment analysis of each post was generally positive (median 0.12, IQR 0.01-0.24). In multivariable logistic regression, the odds of a post expressing negative sentiment were significantly associated with first posts as compared to replies (OR 3.3, 95% CI 2.8 to 3.9, p < 0.001). CONCLUSION We report the development and validation of a new ontology for inflammatory eye diseases, which includes patient-preferred synonyms, and can be used to explore unstructured patient or physician-reported text data, with many potential applications.
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Affiliation(s)
- Samantha C Pendleton
- Institute of Cancer and Genomic Sciences, University of Birmingham, UK; University Hospitals Birmingham NHS Foundation Trust, UK.
| | - Karin Slater
- Institute of Cancer and Genomic Sciences, University of Birmingham, UK; University Hospitals Birmingham NHS Foundation Trust, UK
| | - Andreas Karwath
- Institute of Cancer and Genomic Sciences, University of Birmingham, UK; University Hospitals Birmingham NHS Foundation Trust, UK; Health Data Research, UK
| | - Rose M Gilbert
- Moorfields Eye Hospital NHS Foundation Trust, London, UK; Institute of Ophthalmology, University College London, UK
| | - Nicola Davis
- Olivia's Vision, Southampton Buildings, London, UK
| | - Konrad Pesudovs
- School of Optometry and Vision Science, University of New South Wales, Australia
| | - Xiaoxuan Liu
- University Hospitals Birmingham NHS Foundation Trust, UK; Institute of Inflammation and Ageing, University of Birmingham, UK
| | - Alastair K Denniston
- University Hospitals Birmingham NHS Foundation Trust, UK; Health Data Research, UK; Institute of Inflammation and Ageing, University of Birmingham, UK
| | - Georgios V Gkoutos
- Institute of Cancer and Genomic Sciences, University of Birmingham, UK; University Hospitals Birmingham NHS Foundation Trust, UK; Health Data Research, UK
| | - Tasanee Braithwaite
- University Hospitals Birmingham NHS Foundation Trust, UK; Institute of Applied Health Research, University of Birmingham, UK; The Medical Eye Unit, St Thomas' Hospital NHS Foundation Trust, London, UK
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18
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Norris E, Hastings J, Marques MM, Mutlu ANF, Zink S, Michie S. Why and how to engage expert stakeholders in ontology development: insights from social and behavioural sciences. J Biomed Semantics 2021; 12:4. [PMID: 33757593 PMCID: PMC7985588 DOI: 10.1186/s13326-021-00240-6] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Accepted: 03/09/2021] [Indexed: 12/16/2022] Open
Abstract
BACKGROUND Incorporating the feedback of expert stakeholders in ontology development is important to ensure content is appropriate, comprehensive, meets community needs and is interoperable with other ontologies and classification systems. However, domain experts are often not formally engaged in ontology development, and there is little available guidance on how this involvement should best be conducted and managed. Social and behavioural science studies often involve expert feedback in the development of tools and classification systems but have had little engagement with ontology development. This paper aims to (i) demonstrate how expert feedback can enhance ontology development, and (ii) provide practical recommendations on how to conduct expert feedback in ontology development using methodologies from the social and behavioural sciences. MAIN BODY Considerations for selecting methods for engaging stakeholders are presented. Mailing lists and issue trackers as existing methods used frequently in ontology development are discussed. Advisory boards and working groups, feedback tasks, consensus exercises, discussions and workshops are presented as potential methods from social and behavioural sciences to incorporate in ontology development. CONCLUSIONS A variety of methods from the social and behavioural sciences exist to enable feedback from expert stakeholders in ontology development. Engaging domain experts in ontology development enables depth and clarity in ontology development, whilst also establishing advocates for an ontology upon its completion.
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Affiliation(s)
- Emma Norris
- Health Behaviour Change Research Group, Department of Health Sciences, Brunel University, Uxbridge, UK.
- Centre for Behaviour Change, University College London, London, UK.
| | - Janna Hastings
- Centre for Behaviour Change, University College London, London, UK
| | - Marta M Marques
- Centre for Behaviour Change, University College London, London, UK
- ADAPT SFI Research Centre & Trinity Centre for Practice and Healthcare Innovation, Trinity College, Dublin, Ireland
| | | | - Silje Zink
- Centre for Behaviour Change, University College London, London, UK
- Norwegian National Advisory Unit on Rehabiliation in Rheumatology, Diakonhjemmet Hospital, Oslo, Norway
| | - Susan Michie
- Centre for Behaviour Change, University College London, London, UK
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19
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Use of a modular ontology and a semantic annotation tool to describe the care pathway of patients with amyotrophic lateral sclerosis in a coordination network. PLoS One 2021; 16:e0244604. [PMID: 33406098 PMCID: PMC7787442 DOI: 10.1371/journal.pone.0244604] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2020] [Accepted: 12/11/2020] [Indexed: 11/19/2022] Open
Abstract
The objective of this study was to describe the care pathway of patients with amyotrophic lateral sclerosis (ALS) based on real-life textual data from a regional coordination network, the Ile-de-France ALS network. This coordination network provides care for 92% of patients diagnosed with ALS living in Ile-de-France. We developed a modular ontology (OntoPaRON) for the automatic processing of these unstructured textual data. OntoPaRON has different modules: the core, medical, socio-environmental, coordination, and consolidation modules. Our approach was unique in its creation of fully defined concepts at different levels of the modular ontology to address specific topics relating to healthcare trajectories. We also created a semantic annotation tool specific to the French language and the specificities of our corpus, the Ontology-Based Semantic Annotation Module (OnBaSAM), using the OntoPaRON ontology as a reference. We used these tools to annotate the records of 928 patients automatically. The semantic (qualitative) annotations of the concepts were transformed into quantitative data. By using these pipelines we were able to transform unstructured textual data into structured quantitative data. Based on data processing, semantic annotations, sociodemographic data for the patient and clinical variables, we found that the need and demand for human and technical assistance depend on the initial form of the disease, the motor state, and the patient age. The presence of exhaustion in care management, is related to the patient’s motor and cognitive state.
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Michie S, West R, Finnerty AN, Norris E, Wright AJ, Marques MM, Johnston M, Kelly MP, Thomas J, Hastings J. Representation of behaviour change interventions and their evaluation: Development of the Upper Level of the Behaviour Change Intervention Ontology. Wellcome Open Res 2021; 5:123. [PMID: 33614976 PMCID: PMC7868854 DOI: 10.12688/wellcomeopenres.15902.2] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/22/2020] [Indexed: 12/16/2022] Open
Abstract
Background: Behaviour change interventions (BCI), their contexts and evaluation methods are heterogeneous, making it difficult to synthesise evidence and make recommendations for real-world policy and practice. Ontologies provide a means for addressing this. They represent knowledge formally as entities and relationships using a common language able to cross disciplinary boundaries and topic domains. This paper reports the development of the upper level of the Behaviour Change Intervention Ontology (BCIO), which provides a systematic way to characterise BCIs, their contexts and their evaluations. Methods: Development took place in four steps. (1) Entities and relationships were identified by behavioural and social science experts, based on their knowledge of evidence and theory, and their practical experience of behaviour change interventions and evaluations. (2) The outputs of the first step were critically examined by a wider group of experts, including the study ontology expert and those experienced in annotating relevant literature using the initial ontology entities. The outputs of the second step were tested by (3) feedback from three external international experts in ontologies and (4) application of the prototype upper-level BCIO to annotating published reports; this informed the final development of the upper-level BCIO. Results: The final upper-level BCIO specifies 42 entities, including the BCI scenario, elaborated across 21 entities and 7 relationship types, and the BCI evaluation study comprising 10 entities and 9 relationship types. BCI scenario entities include the behaviour change intervention (content and delivery), outcome behaviour, mechanism of action, and its context, which includes population and setting. These entities have corresponding entities relating to the planning and reporting of interventions and their evaluations. Conclusions: The upper level of the BCIO provides a comprehensive and systematic framework for representing BCIs, their contexts and their evaluations.
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Affiliation(s)
- Susan Michie
- Centre for Behaviour Change, University College London, London, UK
| | - Robert West
- Research Department of Epidemiology & Public Health, University College London, London, UK
| | | | - Emma Norris
- Centre for Behaviour Change, University College London, London, UK
| | - Alison J. Wright
- Centre for Behaviour Change, University College London, London, UK
| | - Marta M. Marques
- Centre for Behaviour Change, University College London, London, UK
- ADAPT SFI Research Centre, Trinity College Dublin, Dublin, Ireland
| | - Marie Johnston
- Aberdeen Health Psychology Group, University of Aberdeen, Aberdeen, UK
| | - Michael P. Kelly
- Primary Care Unit, Institute of Public Health, University of Cambridge, Cambridge, UK
| | - James Thomas
- UCL Institute of Education, University College London, London, UK
| | - Janna Hastings
- Centre for Behaviour Change, University College London, London, UK
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21
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Wright AJ, Norris E, Finnerty AN, Marques MM, Johnston M, Kelly MP, Hastings J, West R, Michie S. Ontologies relevant to behaviour change interventions: a method for their development. Wellcome Open Res 2020; 5:126. [PMID: 33447665 PMCID: PMC7786424 DOI: 10.12688/wellcomeopenres.15908.3] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/10/2020] [Indexed: 12/16/2022] Open
Abstract
Background: Behaviour and behaviour change are integral to many aspects of wellbeing and sustainability. However, reporting behaviour change interventions accurately and synthesising evidence about effective interventions is hindered by lacking a shared, scientific terminology to describe intervention characteristics. Ontologies are standardised frameworks that provide controlled vocabularies to help unify and connect scientific fields. To date, there is no published guidance on the specific methods required to develop ontologies relevant to behaviour change. We report the creation and refinement of a method for developing ontologies that make up the Behaviour Change Intervention Ontology (BCIO). Aims: (1) To describe the development method of the BCIO and explain its rationale; (2) To provide guidance on implementing the activities within the development method. Method and results: The method for developing ontologies relevant to behaviour change interventions was constructed by considering principles of good practice in ontology development and identifying key activities required to follow those principles. The method's details were refined through application to developing two ontologies. The resulting ontology development method involved: (1) defining the ontology's scope; (2) identifying key entities; (3) refining the ontology through an iterative process of literature annotation, discussion and revision; (4) expert stakeholder review; (5) testing inter-rater reliability; (6) specifying relationships between entities, and; (7) disseminating and maintaining the ontology. Guidance is provided for conducting relevant activities for each step. Conclusions: We have developed a detailed method for creating ontologies relevant to behaviour change interventions, together with practical guidance for each step, reflecting principles of good practice in ontology development. The most novel aspects of the method are the use of formal mechanisms for literature annotation and expert stakeholder review to develop and improve the ontology content. We suggest the mnemonic SELAR3, representing the method's first six steps as Scope, Entities, Literature Annotation, Review, Reliability, Relationships.
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Affiliation(s)
- Alison J. Wright
- Centre for Behaviour Change, University College London, London, UK
| | - Emma Norris
- Centre for Behaviour Change, University College London, London, UK
| | | | - Marta M. Marques
- Centre for Behaviour Change, University College London, London, UK
- ADAPT SFI Research Centre, Trinity College Dublin, Dublin, Ireland
| | - Marie Johnston
- Aberdeen Health Psychology Group, University of Aberdeen, Aberdeen, UK
| | - Michael P. Kelly
- Primary Care Unit, Institute of Public Health, University of Cambridge, Cambridge, UK
| | - Janna Hastings
- Centre for Behaviour Change, University College London, London, UK
| | - Robert West
- Research Department of Epidemiology & Public Health, University College London, London, UK
| | - Susan Michie
- Centre for Behaviour Change, University College London, London, UK
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Kersloot MG, van Putten FJP, Abu-Hanna A, Cornet R, Arts DL. Natural language processing algorithms for mapping clinical text fragments onto ontology concepts: a systematic review and recommendations for future studies. J Biomed Semantics 2020; 11:14. [PMID: 33198814 PMCID: PMC7670625 DOI: 10.1186/s13326-020-00231-z] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2020] [Accepted: 11/03/2020] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Free-text descriptions in electronic health records (EHRs) can be of interest for clinical research and care optimization. However, free text cannot be readily interpreted by a computer and, therefore, has limited value. Natural Language Processing (NLP) algorithms can make free text machine-interpretable by attaching ontology concepts to it. However, implementations of NLP algorithms are not evaluated consistently. Therefore, the objective of this study was to review the current methods used for developing and evaluating NLP algorithms that map clinical text fragments onto ontology concepts. To standardize the evaluation of algorithms and reduce heterogeneity between studies, we propose a list of recommendations. METHODS Two reviewers examined publications indexed by Scopus, IEEE, MEDLINE, EMBASE, the ACM Digital Library, and the ACL Anthology. Publications reporting on NLP for mapping clinical text from EHRs to ontology concepts were included. Year, country, setting, objective, evaluation and validation methods, NLP algorithms, terminology systems, dataset size and language, performance measures, reference standard, generalizability, operational use, and source code availability were extracted. The studies' objectives were categorized by way of induction. These results were used to define recommendations. RESULTS Two thousand three hundred fifty five unique studies were identified. Two hundred fifty six studies reported on the development of NLP algorithms for mapping free text to ontology concepts. Seventy-seven described development and evaluation. Twenty-two studies did not perform a validation on unseen data and 68 studies did not perform external validation. Of 23 studies that claimed that their algorithm was generalizable, 5 tested this by external validation. A list of sixteen recommendations regarding the usage of NLP systems and algorithms, usage of data, evaluation and validation, presentation of results, and generalizability of results was developed. CONCLUSION We found many heterogeneous approaches to the reporting on the development and evaluation of NLP algorithms that map clinical text to ontology concepts. Over one-fourth of the identified publications did not perform an evaluation. In addition, over one-fourth of the included studies did not perform a validation, and 88% did not perform external validation. We believe that our recommendations, alongside an existing reporting standard, will increase the reproducibility and reusability of future studies and NLP algorithms in medicine.
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Affiliation(s)
- Martijn G. Kersloot
- Amsterdam UMC, University of Amsterdam, Department of Medical Informatics, Amsterdam Public Health Research Institute Castor EDC, Room J1B-109, PO Box 22700, 1100 DE Amsterdam, The Netherlands
- Castor EDC, Amsterdam, The Netherlands
| | - Florentien J. P. van Putten
- Amsterdam UMC, University of Amsterdam, Department of Medical Informatics, Amsterdam Public Health Research Institute Castor EDC, Room J1B-109, PO Box 22700, 1100 DE Amsterdam, The Netherlands
| | - Ameen Abu-Hanna
- Amsterdam UMC, University of Amsterdam, Department of Medical Informatics, Amsterdam Public Health Research Institute Castor EDC, Room J1B-109, PO Box 22700, 1100 DE Amsterdam, The Netherlands
| | - Ronald Cornet
- Amsterdam UMC, University of Amsterdam, Department of Medical Informatics, Amsterdam Public Health Research Institute Castor EDC, Room J1B-109, PO Box 22700, 1100 DE Amsterdam, The Netherlands
| | - Derk L. Arts
- Amsterdam UMC, University of Amsterdam, Department of Medical Informatics, Amsterdam Public Health Research Institute Castor EDC, Room J1B-109, PO Box 22700, 1100 DE Amsterdam, The Netherlands
- Castor EDC, Amsterdam, The Netherlands
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23
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Thessen AE, Walls RL, Vogt L, Singer J, Warren R, Buttigieg PL, Balhoff JP, Mungall CJ, McGuinness DL, Stucky BJ, Yoder MJ, Haendel MA. Transforming the study of organisms: Phenomic data models and knowledge bases. PLoS Comput Biol 2020; 16:e1008376. [PMID: 33232313 PMCID: PMC7685442 DOI: 10.1371/journal.pcbi.1008376] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023] Open
Abstract
The rapidly decreasing cost of gene sequencing has resulted in a deluge of genomic data from across the tree of life; however, outside a few model organism databases, genomic data are limited in their scientific impact because they are not accompanied by computable phenomic data. The majority of phenomic data are contained in countless small, heterogeneous phenotypic data sets that are very difficult or impossible to integrate at scale because of variable formats, lack of digitization, and linguistic problems. One powerful solution is to represent phenotypic data using data models with precise, computable semantics, but adoption of semantic standards for representing phenotypic data has been slow, especially in biodiversity and ecology. Some phenotypic and trait data are available in a semantic language from knowledge bases, but these are often not interoperable. In this review, we will compare and contrast existing ontology and data models, focusing on nonhuman phenotypes and traits. We discuss barriers to integration of phenotypic data and make recommendations for developing an operationally useful, semantically interoperable phenotypic data ecosystem.
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Affiliation(s)
- Anne E. Thessen
- Environmental and Molecular Toxicology, Oregon State University, Corvallis, Oregon, United States of America
- Ronin Institute for Independent Scholarship, Monclair, New Jersey, United States of America
| | - Ramona L. Walls
- Bio5 Institute, University of Arizona, Tucson, Arizona, United States of America
| | - Lars Vogt
- TIB Leibniz Information Centre for Science and Technology, Hannover, Germany
| | | | | | - Pier Luigi Buttigieg
- Alfred-Wegener-Institut, Helmholtz-Zentrum für Polar- und Meeresforschung, Bremerhaven, Germany
| | - James P. Balhoff
- Renaissance Computing Institute, University of North Carolina, Chapel Hill, North Carolina, United States of America
| | - Christopher J. Mungall
- Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, California, United States of America
| | | | - Brian J. Stucky
- Florida Museum of Natural History, University of Florida, Gainesville, Florida, United States of America
| | - Matthew J. Yoder
- Illinois Natural History Survey, Champaign, Illinois, United States of America
| | - Melissa A. Haendel
- Environmental and Molecular Toxicology, Oregon State University, Corvallis, Oregon, United States of America
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24
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Yıldırım A, Uskudarli S. Microblog topic identification using Linked Open Data. PLoS One 2020; 15:e0236863. [PMID: 32780736 PMCID: PMC7418982 DOI: 10.1371/journal.pone.0236863] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2019] [Accepted: 07/08/2020] [Indexed: 11/18/2022] Open
Abstract
Much valuable information is embedded in social media posts (microposts) which are contributed by a great variety of persons about subjects that of interest to others. The automated utilization of this information is challenging due to the overwhelming quantity of posts and the distributed nature of the information related to subjects across several posts. Numerous approaches have been proposed to detect topics from collections of microposts, where the topics are represented by lists of terms such as words, phrases, or word embeddings. Such topics are used in tasks like classification and recommendations. The interpretation of topics is considered a separate task in such methods, albeit they are becoming increasingly human-interpretable. This work proposes an approach for identifying machine-interpretable topics of collective interest. We define topics as a set of related elements that are associated by having posted in the same contexts. To represent topics, we introduce an ontology specified according to the W3C recommended standards. The elements of the topics are identified via linking entities to resources published on Linked Open Data (LOD). Such representation enables processing topics to provide insights that go beyond what is explicitly expressed in the microposts. The feasibility of the proposed approach is examined by generating topics from more than one million tweets collected from Twitter during various events. The utility of these topics is demonstrated with a variety of topic-related tasks along with a comparison of the effort required to perform the same tasks with words-list-based representations. Manual evaluation of randomly selected 36 sets of topics yielded 81.0% and 93.3% for the precision and F1 scores respectively.
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Affiliation(s)
- Ahmet Yıldırım
- Complex Systems Research Lab, Department of Computer Engineering, Bogazici University, Istanbul, Turkey
- * E-mail:
| | - Suzan Uskudarli
- Complex Systems Research Lab, Department of Computer Engineering, Bogazici University, Istanbul, Turkey
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25
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Wright AJ, Norris E, Finnerty AN, Marques MM, Johnston M, Kelly MP, Hastings J, West R, Michie S. Ontologies relevant to behaviour change interventions: a method for their development. Wellcome Open Res 2020; 5:126. [PMID: 33447665 PMCID: PMC7786424 DOI: 10.12688/wellcomeopenres.15908.2] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/29/2020] [Indexed: 12/16/2022] Open
Abstract
Background: Behaviour and behaviour change are integral to many aspects of wellbeing and sustainability. However, reporting behaviour change interventions accurately and synthesising evidence about effective interventions is hindered by lacking a shared, scientific terminology to describe intervention characteristics. Ontologies are knowledge structures that provide controlled vocabularies to help unify and connect scientific fields. To date, there is no published guidance on the specific methods required to develop ontologies relevant to behaviour change. We report the creation and refinement of a method for developing ontologies that make up the Behaviour Change Intervention Ontology (BCIO). Aims: (1) To describe the development method of the BCIO and explain its rationale; (2) To provide guidance on implementing the activities within the development method. Method and results: The method for developing ontologies relevant to behaviour change interventions was constructed by considering principles of good practice in ontology development and identifying key activities required to follow those principles. The method's details were refined through application to developing two ontologies. The resulting ontology development method involved: (1) defining the ontology's scope; (2) identifying key entities; (3) refining the ontology through an iterative process of literature annotation, discussion and revision; (4) expert stakeholder review; (5) testing inter-rater reliability; (6) specifying relationships between entities, and; (7) disseminating and maintaining the ontology. Guidance is provided for conducting relevant activities for each step. Conclusions: We have developed a detailed method for creating ontologies relevant to behaviour change interventions, together with practical guidance for each step, reflecting principles of good practice in ontology development. The most novel aspects of the method are the use of formal mechanisms for literature annotation and expert stakeholder review to develop and improve the ontology content. We suggest the mnemonic SELAR3, representing the method's first six steps as Scope, Entities, Literature Annotation, Review, Reliability, Relationships.
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Affiliation(s)
- Alison J. Wright
- Centre for Behaviour Change, University College London, London, UK
| | - Emma Norris
- Centre for Behaviour Change, University College London, London, UK
| | | | - Marta M. Marques
- Centre for Behaviour Change, University College London, London, UK
- ADAPT SFI Research Centre, Trinity College Dublin, Dublin, Ireland
| | - Marie Johnston
- Aberdeen Health Psychology Group, University of Aberdeen, Aberdeen, UK
| | - Michael P. Kelly
- Primary Care Unit, Institute of Public Health, University of Cambridge, Cambridge, UK
| | - Janna Hastings
- Centre for Behaviour Change, University College London, London, UK
| | - Robert West
- Research Department of Epidemiology & Public Health, University College London, London, UK
| | - Susan Michie
- Centre for Behaviour Change, University College London, London, UK
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26
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Wright AJ, Norris E, Finnerty AN, Marques MM, Johnston M, Kelly MP, Hastings J, West R, Michie S. Ontologies relevant to behaviour change interventions: a method for their development. Wellcome Open Res 2020; 5:126. [PMID: 33447665 PMCID: PMC7786424 DOI: 10.12688/wellcomeopenres.15908.1] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/20/2020] [Indexed: 12/16/2022] Open
Abstract
Background: Behaviour and behaviour change are integral to many aspects of wellbeing and sustainability. However, reporting behaviour change interventions accurately and synthesising evidence about effective interventions is hindered by lacking a shared, scientific terminology to describe intervention characteristics. Ontologies are knowledge structures that provide controlled vocabularies to help unify and connect scientific fields. To date, there is no published guidance on the specific methods required to develop ontologies relevant to behaviour change. We report the creation and refinement of a method for developing ontologies that make up the Behaviour Change Intervention Ontology (BCIO). Aims: (1) To describe the development method of the BCIO and explain its rationale; (2) To provide guidance on implementing the activities within the development method. Method and results: The method for developing ontologies relevant to behaviour change interventions was constructed by considering principles of good practice in ontology development and identifying key activities required to follow those principles. The method's details were refined through application to developing two ontologies. The resulting ontology development method involved: (1) defining the ontology's scope; (2) identifying key entities; (3) refining the ontology through an iterative process of literature annotation, discussion and revision; (4) expert stakeholder review; (5) testing inter-rater reliability; (6) specifying relationships between entities, and; (7) disseminating and maintaining the ontology. Guidance is provided for conducting relevant activities for each step. Conclusions: We have developed a detailed method for creating ontologies relevant to behaviour change interventions, together with practical guidance for each step, reflecting principles of good practice in ontology development. The most novel aspects of the method are the use of formal mechanisms for literature annotation and expert stakeholder review to develop and improve the ontology content. We suggest the mnemonic SELAR3, representing the method's first six steps as Scope, Entities, Literature Annotation, Review, Reliability, Relationships.
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Affiliation(s)
- Alison J. Wright
- Centre for Behaviour Change, University College London, London, UK
| | - Emma Norris
- Centre for Behaviour Change, University College London, London, UK
| | | | - Marta M. Marques
- Centre for Behaviour Change, University College London, London, UK
- ADAPT SFI Research Centre, Trinity College Dublin, Dublin, Ireland
| | - Marie Johnston
- Aberdeen Health Psychology Group, University of Aberdeen, Aberdeen, UK
| | - Michael P. Kelly
- Primary Care Unit, Institute of Public Health, University of Cambridge, Cambridge, UK
| | - Janna Hastings
- Centre for Behaviour Change, University College London, London, UK
| | - Robert West
- Research Department of Epidemiology & Public Health, University College London, London, UK
| | - Susan Michie
- Centre for Behaviour Change, University College London, London, UK
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27
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Michie S, West R, Finnerty AN, Norris E, Wright AJ, Marques MM, Johnston M, Kelly MP, Thomas J, Hastings J. Representation of behaviour change interventions and their evaluation: Development of the Upper Level of the Behaviour Change Intervention Ontology. Wellcome Open Res 2020; 5:123. [PMID: 33614976 PMCID: PMC7868854 DOI: 10.12688/wellcomeopenres.15902.1] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/20/2020] [Indexed: 12/16/2022] Open
Abstract
Background: Behaviour change interventions (BCI), their contexts and evaluation methods are heterogeneous, making it difficult to synthesise evidence and make recommendations for real-world policy and practice. Ontologies provide a means for addressing this. They represent knowledge formally as entities and relationships using a common language able to cross disciplinary boundaries and topic domains. This paper reports the development of the upper level of the Behaviour Change Intervention Ontology (BCIO), which provides a systematic way to characterise BCIs, their contexts and their evaluations. Methods: Development took place in four steps. (1) Entities and relationships were identified by behavioural and social science experts, based on their knowledge of evidence and theory, and their practical experience of behaviour change interventions and evaluations. (2) The outputs of the first step were critically examined by a wider group of experts, including the study ontology expert and those experienced in annotating relevant literature using the initial ontology entities. The outputs of the second step were tested by (3) feedback from three external international experts in ontologies and (4) application of the prototype upper-level BCIO to annotating published reports; this informed the final development of the upper-level BCIO. Results: The final upper-level BCIO specifies 42 entities, including the BCI scenario, elaborated across 21 entities and 7 relationship types, and the BCI evaluation study comprising 10 entities and 9 relationship types. BCI scenario entities include the behaviour change intervention (content and delivery), outcome behaviour, mechanism of action, and its context, which includes population and setting. These entities have corresponding entities relating to the planning and reporting of interventions and their evaluations. Conclusions: The upper level of the BCIO provides a comprehensive and systematic framework for representing BCIs, their contexts and their evaluations.
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Affiliation(s)
- Susan Michie
- Centre for Behaviour Change, University College London, London, UK
| | - Robert West
- Research Department of Epidemiology & Public Health, University College London, London, UK
| | | | - Emma Norris
- Centre for Behaviour Change, University College London, London, UK
| | - Alison J. Wright
- Centre for Behaviour Change, University College London, London, UK
| | - Marta M. Marques
- Centre for Behaviour Change, University College London, London, UK
- ADAPT SFI Research Centre, Trinity College Dublin, Dublin, Ireland
| | - Marie Johnston
- Aberdeen Health Psychology Group, University of Aberdeen, Aberdeen, UK
| | - Michael P. Kelly
- Primary Care Unit, Institute of Public Health, University of Cambridge, Cambridge, UK
| | - James Thomas
- UCL Institute of Education, University College London, London, UK
| | - Janna Hastings
- Centre for Behaviour Change, University College London, London, UK
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28
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The Hearing Impairment Ontology: A Tool for Unifying Hearing Impairment Knowledge to Enhance Collaborative Research. Genes (Basel) 2019; 10:genes10120960. [PMID: 31766582 PMCID: PMC6947307 DOI: 10.3390/genes10120960] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2019] [Revised: 10/31/2019] [Accepted: 11/11/2019] [Indexed: 02/07/2023] Open
Abstract
Hearing impairment (HI) is a common sensory disorder that is defined as the partial or complete inability to detect sound in one or both ears. This diverse pathology is associated with a myriad of phenotypic expressions and can be non-syndromic or syndromic. HI can be caused by various genetic, environmental, and/or unknown factors. Some ontologies capture some HI forms, phenotypes, and syndromes, but there is no comprehensive knowledge portal which includes aspects specific to the HI disease state. This hampers inter-study comparability, integration, and interoperability within and across disciplines. This work describes the HI Ontology (HIO) that was developed based on the Sickle Cell Disease Ontology (SCDO) model. This is a collaboratively developed resource built around the ‘Hearing Impairment’ concept by a group of experts in different aspects of HI and ontologies. HIO is the first comprehensive, standardized, hierarchical, and logical representation of existing HI knowledge. HIO allows researchers and clinicians alike to readily access standardized HI-related knowledge in a single location and promotes collaborations and HI information sharing, including epidemiological, socio-environmental, biomedical, genetic, and phenotypic information. Furthermore, this ontology illustrates the adaptability of the SCDO framework for use in developing a disease-specific ontology.
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29
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Dhombres F, Charlet J. Formal Medical Knowledge Representation Supports Deep Learning Algorithms, Bioinformatics Pipelines, Genomics Data Analysis, and Big Data Processes. Yearb Med Inform 2019; 28:152-155. [PMID: 31419827 PMCID: PMC6697514 DOI: 10.1055/s-0039-1677933] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
OBJECTIVE To select, present, and summarize the best papers published in 2018 in the field of Knowledge Representation and Management (KRM). METHODS A comprehensive and standardized review of the medical informatics literature was performed to select the most interesting papers published in 2018 in KRM, based on PubMed and ISI Web Of Knowledge queries. RESULTS Four best papers were selected among the 962 publications retrieved following the Yearbook review process. The research areas in 2018 were mainly related to the ontology-based data integration for phenotype-genotype association mining, the design of ontologies and their application, and the semantic annotation of clinical texts. CONCLUSION In the KRM selection for 2018, research on semantic representations demonstrated their added value for enhanced deep learning approaches in text mining and for designing novel bioinformatics pipelines based on graph databases. In addition, the ontology structure can enrich the analyses of whole genome expression data. Finally, semantic representations demonstrated promising results to process phenotypic big data.
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Affiliation(s)
- Ferdinand Dhombres
- Sorbonne Université, Université Paris 13, Sorbonne Paris Cité, INSERM, UMR_S 1142, LIMICS, Paris, France.,Médecine Sorbonne Université, Service de Médecine Fætale, AP-HP/HUEP, Hôpital Armand Trousseau, Paris, France
| | - Jean Charlet
- Sorbonne Université, Université Paris 13, Sorbonne Paris Cité, INSERM, UMR_S 1142, LIMICS, Paris, France.,AP-HP, Delegation for Clinical Research and Innovation, Paris, France
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30
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Ontology mapping for semantically enabled applications. Drug Discov Today 2019; 24:2068-2075. [PMID: 31158512 DOI: 10.1016/j.drudis.2019.05.020] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2018] [Revised: 04/12/2019] [Accepted: 05/28/2019] [Indexed: 12/14/2022]
Abstract
In this review, we provide a summary of recent progress in ontology mapping (OM) at a crucial time when biomedical research is under a deluge of an increasing amount and variety of data. This is particularly important for realising the full potential of semantically enabled or enriched applications and for meaningful insights, such as drug discovery, using machine-learning technologies. We discuss challenges and solutions for better ontology mappings, as well as how to select ontologies before their application. In addition, we describe tools and algorithms for ontology mapping, including evaluation of tool capability and quality of mappings. Finally, we outline the requirements for an ontology mapping service (OMS) and the progress being made towards implementation of such sustainable services.
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31
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Natsiavas P, Malousi A, Bousquet C, Jaulent MC, Koutkias V. Computational Advances in Drug Safety: Systematic and Mapping Review of Knowledge Engineering Based Approaches. Front Pharmacol 2019; 10:415. [PMID: 31156424 PMCID: PMC6533857 DOI: 10.3389/fphar.2019.00415] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2018] [Accepted: 04/02/2019] [Indexed: 12/12/2022] Open
Abstract
Drug Safety (DS) is a domain with significant public health and social impact. Knowledge Engineering (KE) is the Computer Science discipline elaborating on methods and tools for developing “knowledge-intensive” systems, depending on a conceptual “knowledge” schema and some kind of “reasoning” process. The present systematic and mapping review aims to investigate KE-based approaches employed for DS and highlight the introduced added value as well as trends and possible gaps in the domain. Journal articles published between 2006 and 2017 were retrieved from PubMed/MEDLINE and Web of Science® (873 in total) and filtered based on a comprehensive set of inclusion/exclusion criteria. The 80 finally selected articles were reviewed on full-text, while the mapping process relied on a set of concrete criteria (concerning specific KE and DS core activities, special DS topics, employed data sources, reference ontologies/terminologies, and computational methods, etc.). The analysis results are publicly available as online interactive analytics graphs. The review clearly depicted increased use of KE approaches for DS. The collected data illustrate the use of KE for various DS aspects, such as Adverse Drug Event (ADE) information collection, detection, and assessment. Moreover, the quantified analysis of using KE for the respective DS core activities highlighted room for intensifying research on KE for ADE monitoring, prevention and reporting. Finally, the assessed use of the various data sources for DS special topics demonstrated extensive use of dominant data sources for DS surveillance, i.e., Spontaneous Reporting Systems, but also increasing interest in the use of emerging data sources, e.g., observational healthcare databases, biochemical/genetic databases, and social media. Various exemplar applications were identified with promising results, e.g., improvement in Adverse Drug Reaction (ADR) prediction, detection of drug interactions, and novel ADE profiles related with specific mechanisms of action, etc. Nevertheless, since the reviewed studies mostly concerned proof-of-concept implementations, more intense research is required to increase the maturity level that is necessary for KE approaches to reach routine DS practice. In conclusion, we argue that efficiently addressing DS data analytics and management challenges requires the introduction of high-throughput KE-based methods for effective knowledge discovery and management, resulting ultimately, in the establishment of a continuous learning DS system.
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Affiliation(s)
- Pantelis Natsiavas
- Institute of Applied Biosciences, Centre for Research and Technology Hellas, Thessaloniki, Greece.,Sorbonne Université, INSERM, Univ Paris 13, Laboratoire d'Informatique Médicale et d'Ingénierie des Connaissances pour la e-Santé, LIMICS, Paris, France
| | - Andigoni Malousi
- Laboratory of Biological Chemistry, Department of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Cédric Bousquet
- Sorbonne Université, INSERM, Univ Paris 13, Laboratoire d'Informatique Médicale et d'Ingénierie des Connaissances pour la e-Santé, LIMICS, Paris, France.,Public Health and Medical Information Unit, University Hospital of Saint-Etienne, Saint-Étienne, France
| | - Marie-Christine Jaulent
- Sorbonne Université, INSERM, Univ Paris 13, Laboratoire d'Informatique Médicale et d'Ingénierie des Connaissances pour la e-Santé, LIMICS, Paris, France
| | - Vassilis Koutkias
- Institute of Applied Biosciences, Centre for Research and Technology Hellas, Thessaloniki, Greece
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Monnin P, Legrand J, Husson G, Ringot P, Tchechmedjiev A, Jonquet C, Napoli A, Coulet A. PGxO and PGxLOD: a reconciliation of pharmacogenomic knowledge of various provenances, enabling further comparison. BMC Bioinformatics 2019; 20:139. [PMID: 30999867 PMCID: PMC6471679 DOI: 10.1186/s12859-019-2693-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022] Open
Abstract
Background Pharmacogenomics (PGx) studies how genomic variations impact variations in drug response phenotypes. Knowledge in pharmacogenomics is typically composed of units that have the form of ternary relationships gene variant – drug – adverse event. Such a relationship states that an adverse event may occur for patients having the specified gene variant and being exposed to the specified drug. State-of-the-art knowledge in PGx is mainly available in reference databases such as PharmGKB and reported in scientific biomedical literature. But, PGx knowledge can also be discovered from clinical data, such as Electronic Health Records (EHRs), and in this case, may either correspond to new knowledge or confirm state-of-the-art knowledge that lacks “clinical counterpart” or validation. For this reason, there is a need for automatic comparison of knowledge units from distinct sources. Results In this article, we propose an approach, based on Semantic Web technologies, to represent and compare PGx knowledge units. To this end, we developed PGxO, a simple ontology that represents PGx knowledge units and their components. Combined with PROV-O, an ontology developed by the W3C to represent provenance information, PGxO enables encoding and associating provenance information to PGx relationships. Additionally, we introduce a set of rules to reconcile PGx knowledge, i.e. to identify when two relationships, potentially expressed using different vocabularies and levels of granularity, refer to the same, or to different knowledge units. We evaluated our ontology and rules by populating PGxO with knowledge units extracted from PharmGKB (2701), the literature (65,720) and from discoveries reported in EHR analysis studies (only 10, manually extracted); and by testing their similarity. We called PGxLOD (PGx Linked Open Data) the resulting knowledge base that represents and reconciles knowledge units of those various origins. Conclusions The proposed ontology and reconciliation rules constitute a first step toward a more complete framework for knowledge comparison in PGx. In this direction, the experimental instantiation of PGxO, named PGxLOD, illustrates the ability and difficulties of reconciling various existing knowledge sources. Electronic supplementary material The online version of this article (10.1186/s12859-019-2693-9) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Pierre Monnin
- Université de Lorraine, CNRS, Inria, LORIA, Nancy, 54000, France.
| | - Joël Legrand
- Université de Lorraine, CNRS, Inria, LORIA, Nancy, 54000, France
| | - Graziella Husson
- Université de Lorraine, CNRS, Inria, LORIA, Nancy, 54000, France
| | - Patrice Ringot
- Université de Lorraine, CNRS, Inria, LORIA, Nancy, 54000, France
| | | | - Clément Jonquet
- LIRMM, Université de Montpellier, CNRS, Montpellier, 34095, France.,Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, 94305, California, USA
| | - Amedeo Napoli
- Université de Lorraine, CNRS, Inria, LORIA, Nancy, 54000, France
| | - Adrien Coulet
- Université de Lorraine, CNRS, Inria, LORIA, Nancy, 54000, France.,Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, 94305, California, USA
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A scoping review of ontologies related to human behaviour change. Nat Hum Behav 2019; 3:164-172. [PMID: 30944444 DOI: 10.1038/s41562-018-0511-4] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2018] [Accepted: 12/06/2018] [Indexed: 12/16/2022]
Abstract
Ontologies are classification systems specifying entities, definitions and inter-relationships for a given domain, with the potential to advance knowledge about human behaviour change. A scoping review was conducted to: (1) identify what ontologies exist related to human behaviour change, (2) describe the methods used to develop these ontologies and (3) assess the quality of identified ontologies. Using a systematic search, 2,303 papers were identified. Fifteen ontologies met the eligibility criteria for inclusion, developed in areas such as cognition, mental disease and emotions. Methods used for developing the ontologies were expert consultation, data-driven techniques and reuse of terms from existing taxonomies, terminologies and ontologies. Best practices used in ontology development and maintenance were documented. The review did not identify any ontologies representing the breadth and detail of human behaviour change. This suggests that advancing behavioural science would benefit from the development of a behaviour change intervention ontology.
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Smaili FZ, Gao X, Hoehndorf R. OPA2Vec: combining formal and informal content of biomedical ontologies to improve similarity-based prediction. Bioinformatics 2018; 35:2133-2140. [DOI: 10.1093/bioinformatics/bty933] [Citation(s) in RCA: 65] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2018] [Revised: 11/02/2018] [Accepted: 11/07/2018] [Indexed: 12/11/2022] Open
Affiliation(s)
- Fatima Zohra Smaili
- Computer, Electrical & Mathematical Sciences and Engineering (CEMSE) Division, Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
| | - Xin Gao
- Computer, Electrical & Mathematical Sciences and Engineering (CEMSE) Division, Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
| | - Robert Hoehndorf
- Computer, Electrical & Mathematical Sciences and Engineering (CEMSE) Division, Computational Bioscience Research Center (CBRC), King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia
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Jonquet C, Toulet A, Dutta B, Emonet V. Harnessing the Power of Unified Metadata in an Ontology Repository: The Case of AgroPortal. JOURNAL ON DATA SEMANTICS 2018. [DOI: 10.1007/s13740-018-0091-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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