Results for 'biomedical ontologies'

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  1. Putting Biomedical Ontologies to Work.Barry Smith & Mathias Brochhausen - 2010 - Methods of Information in Medicine 49 (2):135-40.
    Biomedical ontologies exist to serve integration of clinical and experimental data, and it is critical to their success that they be put to widespread use in the annotation of data. How, then, can ontologies achieve the sort of user-friendliness, reliability, cost-effectiveness, and breadth of coverage that is necessary to ensure extensive usage? Methods: Our focus here is on two different sets of answers to these questions that have been proposed, on the one hand in medicine, by the (...)
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  2. Biomedical Ontologies.Barry Smith - 2022 - In Peter L. Elkin (ed.), Terminology, Ontology and Their Implementations: Teaching Guide and Notes. Springer. pp. 125-169.
    We begin at the beginning, with an outline of Aristotle’s views on ontology and with a discussion of the influence of these views on Linnaeus. We move from there to consider the data standardization initiatives launched in the 19th century, and then turn to investigate how the idea of computational ontologies developed in the AI and knowledge representation communities in the closing decades of the 20th century. We show how aspects of this idea, particularly those relating to the use (...)
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  3. Biomedical ontology alignment: An approach based on representation learning.Prodromos Kolyvakis, Alexandros Kalousis, Barry Smith & Dimitris Kiritsis - 2018 - Journal of Biomedical Semantics 9 (21).
    While representation learning techniques have shown great promise in application to a number of different NLP tasks, they have had little impact on the problem of ontology matching. Unlike past work that has focused on feature engineering, we present a novel representation learning approach that is tailored to the ontology matching task. Our approach is based on embedding ontological terms in a high-dimensional Euclidean space. This embedding is derived on the basis of a novel phrase retrofitting strategy through which semantic (...)
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  4. The National Center for Biomedical Ontology.Mark A. Musen, Natalya F. Noy, Nigam H. Shah, Patricia L. Whetzel, Christopher G. Chute, Margaret-Anne Story & Barry Smith - 2012 - Journal of the American Medical Informatics Association 19 (2):190-195.
    The National Center for Biomedical Ontology is now in its seventh year. The goals of this National Center for Biomedical Computing are to: create and maintain a repository of biomedical ontologies and terminologies; build tools and web services to enable the use of ontologies and terminologies in clinical and translational research; educate their trainees and the scientific community broadly about biomedical ontology and ontology-based technology and best practices; and collaborate with a variety of groups (...)
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  5. National Center for Biomedical Ontology: Advancing biomedicine through structured organization of scientific knowledge.Daniel L. Rubin, Suzanna E. Lewis, Chris J. Mungall, Misra Sima, Westerfield Monte, Ashburner Michael, Christopher G. Chute, Ida Sim, Harold Solbrig, M. A. Storey, Barry Smith, John D. Richter, Natasha Noy & Mark A. Musen - 2006 - Omics: A Journal of Integrative Biology 10 (2):185-198.
    The National Center for Biomedical Ontology is a consortium that comprises leading informaticians, biologists, clinicians, and ontologists, funded by the National Institutes of Health (NIH) Roadmap, to develop innovative technology and methods that allow scientists to record, manage, and disseminate biomedical information and knowledge in machine-processable form. The goals of the Center are (1) to help unify the divergent and isolated efforts in ontology development by promoting high quality open-source, standards-based tools to create, manage, and use ontologies, (...)
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  6. A realism-based approach to the evolution of biomedical ontologies.Barry Smith - 2006 - In Proceedings of the Annual AMIA Symposium. Washington, DC: American Medical Informatics Association. pp. 121-125.
    We present a novel methodology for calculating the improvements obtained in successive versions of biomedical ontologies. The theory takes into account changes both in reality itself and in our understanding of this reality. The successful application of the theory rests on the willingness of ontology authors to document changes they make by following a number of simple rules. The theory provides a pathway by which ontology authoring can become a science rather than an art, following principles analogous to (...)
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  7. Relations in Biomedical Ontologies.Barry Smith, Werner Ceusters, Bert Klagges, Jacob Köhler, Anand Kuma, Jane Lomax, Chris Mungall, , Fabian Neuhaus, Alan Rector & Cornelius Rosse - 2005 - Genome Biology 6 (5):R46.
    To enhance the treatment of relations in biomedical ontologies we advance a methodology for providing consistent and unambiguous formal definitions of the relational expressions used in such ontologies in a way designed to assist developers and users in avoiding errors in coding and annotation. The resulting Relation Ontology can promote interoperability of ontologies and support new types of automated reasoning about the spatial and temporal dimensions of biological and medical phenomena.
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  8. Towards Interoperability of Biomedical Ontologies - Report Number 07132.Mark Musen, Michael Schroeder & Barry Smith - 2008 - In Towards Interoperability of Biomedical Ontologies. Schloss Dagstuhl-Leibniz-Zentrum Fuer Informatik.
    The meeting focused on uses of ontologies, with a special focus on spatial ontologies, in addressing the ever increasing needs faced by biology and medicine to cope with ever expanding quantities of data. To provide effective solutions computers need to integrate data deriving from myriad heterogeneous sources by bringing the data together within a single framework. The meeting brought together leaders in the field of what are called "top-level ontologies" to address this issue, and to establish strategies (...)
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  9. Clonal complexes in biomedical ontologies.Albert Goldfain, Lindsay Cowell & Barry Smith - 2009 - In Barry Smith (ed.), ICBO 2009: Proceedings of the First International Conference on Biomedical Ontology. Buffalo: NCOR. pp. 168.
    An accurate classification of bacteria is essential for the proper identification of patient infections and subsequent treatment decisions. Multi-Locus Sequence Typing (MLST) is a genetic technique for bacterial classification. MLST classifications are used to cluster bacteria into clonal complexes. Importantly, clonal complexes can serve as a biological species concept for bacteria, facilitating an otherwise difficult taxonomic classification. In this paper, we argue for the inclusion of terms relating to clonal complexes in biomedical ontologies.
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  10. The National Center for Biomedical Ontology: Advancing Biomedicine through Structured Organization of Scientific Knowledge. Rubin - 2012 - Journal of the American Medical Informatics Association 19 (2):190-195.
    The National Center for Biomedical Ontology is now in its seventh year. The goals of this National Center for Biomedical Computing are to: create and maintain a repository of biomedical ontologies and terminologies; build tools and web services to enable the use of ontologies and terminologies in clinical and translational research; educate their trainees and the scientific community broadly about biomedical ontology and ontology-based technology and best practices; and collaborate with a variety of groups (...)
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  11. Towards Interoperability of Biomedical Ontologies.Musen Mark, A. Schroeder, Michael Smith & Barry - 2008 - Schloss Dagstuhl: Leibniz-Zentrum für Informatik.
    Report on Dagstuhl Seminar 07132, Schloss Dagstuhl, March 27-30 , 2007.
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  12. Classificatory Theory in Data-intensive Science: The Case of Open Biomedical Ontologies.Sabina Leonelli - 2012 - International Studies in the Philosophy of Science 26 (1):47 - 65.
    Knowledge-making practices in biology are being strongly affected by the availability of data on an unprecedented scale, the insistence on systemic approaches and growing reliance on bioinformatics and digital infrastructures. What role does theory play within data-intensive science, and what does that tell us about scientific theories in general? To answer these questions, I focus on Open Biomedical Ontologies, digital classification tools that have become crucial to sharing results across research contexts in the biological and biomedical sciences, (...)
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  13. On the proper treatment of pathologies in biomedical ontologies.Barry Smith & Anand Kumar - 2005 - In Barry Smith & Anand Kumar (eds.), Proceedings of the Bio-Ontologies Workshop, Intelligent Systems for Molecular Biology (ISMB 2005). Detroit: pp. 22-23.
    In previous work on biomedical ontologies we showed how the provision of formal definitions for relations such as is_a and part_of can support new types of auto-mated reasoning about biomedical phenomena. We here extend this approach to the transformation_of characteristic of pathologies.
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  14. The Role of Foundational Relations in the Alignment of Biomedical Ontologies.Barry Smith & Cornelius Rosse - 2004 - In M. Fieschi, E. Coiera & Y.-C. J. Li (eds.), Medinfo. IOS Press. pp. 444-448.
    The Foundational Model of Anatomy (FMA) symbolically represents the structural organization of the human body from the macromolecular to the macroscopic levels, with the goal of providing a robust and consistent scheme for classifying anatomical entities that is designed to serve as a reference ontology in biomedical informatics. Here we articulate the need for formally clarifying the is-a and part-of relations in the FMA and similar ontology and terminology systems. We diagnose certain characteristic errors in the treatment of these (...)
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  15. Negative findings in electronic health records and biomedical ontologies: a realist approach.Werner Ceusters, Peter Elkin & Barry Smith - 2007 - International Journal of Medical Informatics 76 (3):S326-S333.
    PURPOSE—A substantial fraction of the observations made by clinicians and entered into patient records are expressed by means of negation or by using terms which contain negative qualifiers (as in “absence of pulse” or “surgical procedure not performed”). This seems at first sight to present problems for ontologies, terminologies and data repositories that adhere to a realist view and thus reject any reference to putative non-existing entities. Basic Formal Ontology (BFO) and Referent Tracking (RT) are examples of such paradigms. (...)
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  16. A strategy for improving and integrating biomedical ontologies.Cornelius Rosse, Anand Kumar, Jose L. V. Mejino, Daniel L. Cook, Landon T. Detwiler & Barry Smith - 2005 - In Proceedings of the Annual Symposium of the American Medical Informatics Association. AMIA. pp. 639-643.
    The integration of biomedical terminologies is indispensable to the process of information integration. When terminologies are linked merely through the alignment of their leaf terms, however, differences in context and ontological structure are ignored. Making use of the SNAP and SPAN ontologies, we show how three reference domain ontologies can be integrated at a higher level, through what we shall call the OBR framework (for: Ontology of Biomedical Reality). OBR is designed to facilitate inference across the (...)
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  17. ICBO 2009: Proceedings of the First International Conference on Biomedical Ontology.Barry Smith (ed.) - 2009 - Buffalo: NCOR.
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  18. The Ontology for Biomedical Investigations.Anita Bandrowski, Ryan Brinkman, Mathias Brochhausen, Matthew H. Brush, Bill Bug, Marcus C. Chibucos, Kevin Clancy, Mélanie Courtot, Dirk Derom, Michel Dumontier, Liju Fan, Jennifer Fostel, Gilberto Fragoso, Frank Gibson, Alejandra Gonzalez-Beltran, Melissa A. Haendel, Yongqun He, Mervi Heiskanen, Tina Hernandez-Boussard, Mark Jensen, Yu Lin, Allyson L. Lister, Phillip Lord, James Malone, Elisabetta Manduchi, Monnie McGee, Norman Morrison, James A. Overton, Helen Parkinson, Bjoern Peters, Philippe Rocca-Serra, Alan Ruttenberg, Susanna-Assunta Sansone, Richard H. Scheuermann, Daniel Schober, Barry Smith, Larisa N. Soldatova, Christian J. Stoeckert, Chris F. Taylor, Carlo Torniai, Jessica A. Turner, Randi Vita, Patricia L. Whetzel & Jie Zheng - 2016 - PLoS ONE 11 (4):e0154556.
    The Ontology for Biomedical Investigations (OBI) is an ontology that provides terms with precisely defined meanings to describe all aspects of how investigations in the biological and medical domains are conducted. OBI re-uses ontologies that provide a representation of biomedical knowledge from the Open Biological and Biomedical Ontologies (OBO) project and adds the ability to describe how this knowledge was derived. We here describe the state of OBI and several applications that are using it, such (...)
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  19. Biomedical Terminologies and Ontologies: Enabling Biomedical Semantic Interoperability and Standards in Europe.Bernard de Bono, Mathias Brochhausen, Sybo Dijkstra, Dipak Kalra, Stephan Keifer & Barry Smith - 2009 - In Bernard de Bono, Mathias Brochhausen, Sybo Dijkstra, Dipak Kalra, Stephan Keifer & Barry Smith (eds.), European Large-Scale Action on Electronic Health.
    In the management of biomedical data, vocabularies such as ontologies and terminologies (O/Ts) are used for (i) domain knowledge representation and (ii) interoperability. The knowledge representation role supports the automated reasoning on, and analysis of, data annotated with O/Ts. At an interoperability level, the use of a communal vocabulary standard for a particular domain is essential for large data repositories and information management systems to communicate consistently with one other. Consequently, the interoperability benefit of selecting a particular O/T (...)
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  20. Biomedical imaging ontologies: A survey and proposal for future work.Barry Smith, Sivaram Arabandi, Mathias Brochhausen, Michael Calhoun, Paolo Ciccarese, Scott Doyle, Bernard Gibaud, Ilya Goldberg, Charles E. Kahn Jr, James Overton, John Tomaszewski & Metin Gurcan - 2015 - Journal of Pathology Informatics 6 (37):37.
    Ontology is one strategy for promoting interoperability of heterogeneous data through consistent tagging. An ontology is a controlled structured vocabulary consisting of general terms (such as “cell” or “image” or “tissue” or “microscope”) that form the basis for such tagging. These terms are designed to represent the types of entities in the domain of reality that the ontology has been devised to capture; the terms are provided with logical defi nitions thereby also supporting reasoning over the tagged data. Aim: This (...)
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  21. Ontological theory for ontological engineering: Biomedical systems information integration.James M. Fielding, Jonathan Simon, Werner Ceusters & Barry Smith - 2004 - In Fielding James M., Simon Jonathan, Ceusters Werner & Smith Barry (eds.), Proceedings of the Ninth International Conference on the Principles of Knowledge Representation and Reasoning (KR2004), Whistler, BC, 2-5 June 2004. pp. 114–120.
    Software application ontologies have the potential to become the keystone in state-of-the-art information management techniques. It is expected that these ontologies will support the sort of reasoning power required to navigate large and complex terminologies correctly and efficiently. Yet, there is one problem in particular that continues to stand in our way. As these terminological structures increase in size and complexity, and the drive to integrate them inevitably swells, it is clear that the level of consistency required for (...)
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  22. Ontology-assisted database integration to support natural language processing and biomedical data-mining.Jean-Luc Verschelde, Marianna C. Santos, Tom Deray, Barry Smith & Werner Ceusters - 2004 - Journal of Integrative Bioinformatics. Repr. In: Yearbook of Bioinformatics , 39–48 1:1-10.
    Successful biomedical data mining and information extraction require a complete picture of biological phenomena such as genes, biological processes, and diseases; as these exist on different levels of granularity. To realize this goal, several freely available heterogeneous databases as well as proprietary structured datasets have to be integrated into a single global customizable scheme. We will present a tool to integrate different biological data sources by mapping them to a proprietary biomedical ontology that has been developed for the (...)
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  23. Biodynamic Ontology: Applying BFO in the Biomedical Domain.Barry Smith, Pierre Grenon & Louis Goldberg - 2004 - Studies in Health and Technology Informatics 102:20–38.
    Current approaches to formal representation in biomedicine are characterized by their focus on either the static or the dynamic aspects of biological reality. We here outline a theory that combines both perspectives and at the same time tackles the by no means trivial issue of their coherent integration. Our position is that a good ontology must be capable of accounting for reality both synchronically (as it exists at a time) and diachronically (as it unfolds through time), but that these are (...)
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  24. A Unified Framework for Biomedical Terminologies and Ontologies.Werner Ceusters & Barry Smith - 2010 - Studies in Health Technology and Informatics 160:1050-1054.
    The goal of the OBO (Open Biomedical Ontologies) Foundry initiative is to create and maintain an evolving collection of non-overlapping interoperable ontologies that will offer unambiguous representations of the types of entities in biological and biomedical reality. These ontologies are designed to serve non-redundant annotation of data and scientific text. To achieve these ends, the Foundry imposes strict requirements upon the ontologies eligible for inclusion. While these requirements are not met by most existing (...) terminologies, the latter may nonetheless support the Foundry’s goal of consistent and non-redundant annotation if appropriate mappings of data annotated with their aid can be achieved. To construct such mappings in reliable fashion, however, it is necessary to analyze terminological resources from an ontologically realistic perspective in such a way as to identify the exact import of the ‘concepts’ and associated terms which they contain. We propose a framework for such analysis that is designed to maximize the degree to which legacy terminologies and the data coded with their aid can be successfully used for information-driven clinical and translational research. (shrink)
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  25. Ontology as the core discipline of biomedical informatics: Legacies of the past and recommendations for the future direction of research.Barry Smith & Werner Ceusters - 2007 - In Gordana Dodig Crnkovic & Susan Stuart (eds.), Computation, Information, Cognition: The Nexus and the Liminal. Cambridge Scholars Publishing. pp. 104-122.
    The automatic integration of rapidly expanding information resources in the life sciences is one of the most challenging goals facing biomedical research today. Controlled vocabularies, terminologies, and coding systems play an important role in realizing this goal, by making it possible to draw together information from heterogeneous sources – for example pertaining to genes and proteins, drugs and diseases – secure in the knowledge that the same terms will also represent the same entities on all occasions of use. In (...)
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  26. Formal ontology for biomedical knowledge systems integration.J. M. Fielding, J. Simon & Barry Smith - 2004 - Proceedings of Euromise:12-17.
    The central hypothesis of the collaboration between Language and Computing (L&C) and the Institute for Formal Ontology and Medical Information Science (IFOMIS) is that the methodology and conceptual rigor of a philosophically inspired formal ontology will greatly benefit software application ontologies. To this end LinKBase®, L&C’s ontology, which is designed to integrate and reason across various external databases simultaneously, has been submitted to the conceptual demands of IFOMIS’s Basic Formal Ontology (BFO). With this, we aim to move beyond the (...)
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  27. Formal Ontology for Natural Language Processing and the Integration of Biomedical Databases.Jonathan Simon, James M. Fielding, Mariana C. Dos Santos & Barry Smith - 2005 - International Journal of Medical Informatics 75 (3-4):224-231.
    The central hypothesis of the collaboration between Language and Computing (L&C) and the Institute for Formal Ontology and Medical Information Science (IFOMIS) is that the methodology and conceptual rigor of a philosophically inspired formal ontology greatly benefits application ontologies. To this end r®, L&C’s ontology, which is designed to integrate and reason across various external databases simultaneously, has been submitted to the conceptual demands of IFOMIS’s Basic Formal Ontology (BFO). With this project we aim to move beyond the level (...)
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  28. An ontology-based methodology for the migration of biomedical terminologies to electronic health records.Barry Smith & Werner Ceusters - 2005 - In Smith Barry & Ceusters Werner (eds.), Proceedings of AMIA Symposium 2005, Washington DC,. AMIA. pp. 704-708.
    Biomedical terminologies are focused on what is general, Electronic Health Records (EHRs) on what is particular, and it is commonly assumed that the step from the one to the other is unproblematic. We argue that this is not so, and that, if the EHR of the future is to fulfill its promise, then the foundations of both EHR architectures and biomedical terminologies need to be reconceived. We accordingly describe a new framework for the treatment of both generals and (...)
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  29. The Environment Ontology: Contextualising biological and biomedical entities.Pier Luigi Buttigieg, Norman Morrison, Barry Smith, Christopher J. Mungall & Suzanna E. Lewis - 2013 - Journal of Biomedical Semantics 4 (43):1-9.
    As biological and biomedical research increasingly reference the environmental context of the biological entities under study, the need for formalisation and standardisation of environment descriptors is growing. The Environment Ontology (ENVO) is a community-led, open project which seeks to provide an ontology for specifying a wide range of environments relevant to multiple life science disciplines and, through an open participation model, to accommodate the terminological requirements of all those needing to annotate data using ontology classes. This paper summarises ENVO’s (...)
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  30. Protein-centric connection of biomedical knowledge: Protein Ontology research and annotation tools.Cecilia N. Arighi, Darren A. Natale, Judith A. Blake, Carol J. Bult, Michael Caudy, Alexander D. Diehl, Harold J. Drabkin, Peter D'Eustachio, Alexei Evsikov, Hongzhan Huang, Barry Smith & Others - 2011 - In Proceedings of the 2nd International Conference on Biomedical Ontology. Buffalo, NY: NCOR. pp. 285-287.
    The Protein Ontology (PRO) web resource provides an integrative framework for protein-centric exploration and enables specific and precise annotation of proteins and protein complexes based on PRO. Functionalities include: browsing, searching and retrieving, terms, displaying selected terms in OBO or OWL format, and supporting URIs. In addition, the PRO website offers multiple ways for the user to request, submit, or modify terms and/or annotation. We will demonstrate the use of these tools for protein research and annotation.
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  31. Towards a Reference Terminology for Ontology Research and Development in the Biomedical Domain.Barry Smith, Waclaw Kusnierczyk, Daniel Schober, & Werner Ceusters - 2006 - In Barry Smith, Waclaw Kusnierczyk, Schober & Werner Ceusters (eds.), Proceedings of KR-MED, CEUR, vol. 222. pp. 57-65.
    Ontology is a burgeoning field, involving researchers from the computer science, philosophy, data and software engineering, logic, linguistics, and terminology domains. Many ontology-related terms with precise meanings in one of these domains have different meanings in others. Our purpose here is to initiate a path towards disambiguation of such terms. We draw primarily on the literature of biomedical informatics, not least because the problems caused by unclear or ambiguous use of terms have been there most thoroughly addressed. We advance (...)
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  32. Building Ontologies with Basic Formal Ontology.Robert Arp, Barry Smith & Andrew D. Spear - 2015 - Cambridge, MA: MIT Press.
    In the era of “big data,” science is increasingly information driven, and the potential for computers to store, manage, and integrate massive amounts of data has given rise to such new disciplinary fields as biomedical informatics. Applied ontology offers a strategy for the organization of scientific information in computer-tractable form, drawing on concepts not only from computer and information science but also from linguistics, logic, and philosophy. This book provides an introduction to the field of applied ontology that is (...)
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  33. Strengths and Limitations of Formal Ontologies in the Biomedical Domain.Barry Smith - 2009 - Electronic Journal of Communication, Information and Innovation in Health 3 (1):31-45.
    We propose a typology of representational artifacts for health care and life sciences domains and associate this typology with different kinds of formal ontology and logic, drawing conclusions as to the strengths and limitations for ontology in a description logics framework. The four types of domain representation we consider are: (i) lexico-semantic representation, (ii) representation of types of entities, (iii) representations of background knowledge, and (iv) representation of individuals. We advocate a clear distinction of the four kinds of representation in (...)
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  34. Saliva Ontology: An ontology-based framework for a Salivaomics Knowledge Base.Jiye Ai, Barry Smith & David Wong - 2010 - BMC Bioinformatics 11 (1):302.
    The Salivaomics Knowledge Base (SKB) is designed to serve as a computational infrastructure that can permit global exploration and utilization of data and information relevant to salivaomics. SKB is created by aligning (1) the saliva biomarker discovery and validation resources at UCLA with (2) the ontology resources developed by the OBO (Open Biomedical Ontologies) Foundry, including a new Saliva Ontology (SALO). We define the Saliva Ontology (SALO; http://www.skb.ucla.edu/SALO/) as a consensus-based controlled vocabulary of terms and relations dedicated to (...)
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  35. Creating a Controlled Vocabulary for the Ethics of Human Research: Towards a biomedical ethics ontology.David Koepsell, Robert Arp, Jennifer Fostel & Barry Smith - 2009 - Journal of Empirical Research on Human Research Ethics 4 (1):43-58.
    Ontologies describe reality in specific domains in ways that can bridge various disciplines and languages. They allow easier access and integration of information that is collected by different groups. Ontologies are currently used in the biomedical sciences, geography, and law. A Biomedical Ethics Ontology would benefit members of ethics committees who deal with protocols and consent forms spanning numerous fields of inquiry. There already exists the Ontology for Biomedical Investigations (OBI); the proposed BMEO would interoperate (...)
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  36. Bioportal: Ontologies and integrated data resources at the click of the mouse.L. Whetzel Patricia, H. Shah Nigam, F. Noy Natalya, Dai Benjamin, Dorf Michael, Griffith Nicholas, Jonquet Clement, Youn Cherie, Callendar Chris, Coulet Adrien, Barry Smith, Chris Chute & Mark Musen - 2011 - In Whetzel Patricia L., Shah Nigam H., Noy Natalya F., Benjamin Dai, Michael Dorf, Nicholas Griffith, Clement Jonquet, Cherie Youn, Chris Callendar, Adrien Coulet, Smith Barry, Chute Chris & Musen Mark (eds.), Proceedings of the 2nd International Conference on Biomedical Ontology, Buffalo, NY. pp. 292-293.
    BioPortal is a Web portal that provides access to a library of biomedical ontologies and terminologies developed in OWL, RDF(S), OBO format, Protégé frames, and Rich Release Format. BioPortal functionality, driven by a service-oriented architecture, includes the ability to browse, search and visualize ontologies (Figure 1). The Web interface also facilitates community-based participation in the evaluation and evolution of ontology content.
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  37. Ontology based annotation of contextualized vital signs.Goldfain Albert, Xu Min, Bona Jonathan & Barry Smith - 2013 - In Proceedings of the Fourth International Conference on Biomedical Ontology (ICBO). Montreal: pp. 28-33.
    Representing the kinetic state of a patient (posture, motion, and activity) during vital sign measurement is an important part of continuous monitoring applications, especially remote monitoring applications. In contextualized vital sign representation, the measurement result is presented in conjunction with salient measurement context metadata. We present an automated annotation system for vital sign measurements that uses ontologies from the Open Biomedical Ontology Foundry (OBO Foundry) to represent the patient’s kinetic state at the time of measurement. The annotation system (...)
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  38. The Protein Ontology: A structured representation of protein forms and complexes.Darren Natale, Cecilia N. Arighi, Winona C. Barker, Judith A. Blake, Carol J. Bult, Michael Caudy, Harold J. Drabkin, Peter D’Eustachio, Alexei V. Evsikov, Hongzhan Huang, Jules Nchoutmboube, Natalia V. Roberts, Barry Smith, Jian Zhang & Cathy H. Wu - 2011 - Nucleic Acids Research 39 (1):D539-D545.
    The Protein Ontology (PRO) provides a formal, logically-based classification of specific protein classes including structured representations of protein isoforms, variants and modified forms. Initially focused on proteins found in human, mouse and Escherichia coli, PRO now includes representations of protein complexes. The PRO Consortium works in concert with the developers of other biomedical ontologies and protein knowledge bases to provide the ability to formally organize and integrate representations of precise protein forms so as to enhance accessibility to results (...)
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  39. Biomedical informatics and granularity.Anand Kumar & Barry Smith - 2004 - Comparative and Functional Genomics 5 (6-7):501-508.
    An explicit formal-ontological representation of entities existing at multiple levels of granularity is an urgent requirement for biomedical information processing. We discuss some fundamental principles which can form a basis for such a representation. We also comment on some of the implicit treatments of granularity in currently available ontologies and terminologies (GO, FMA, SNOMED CT).
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  40. The Ontology-Epistemology Divide: A Case Study in Medical Terminology.OIivier Bodenreider, Barry Smith & Anita Burgun - 2004 - In Achille Varzi & Laure Vieu (eds.), Formal Ontology in Information Systems. Proceedings of the Third International Conference (FOIS 2004). IOS Press.
    Medical terminology collects and organizes the many different kinds of terms employed in the biomedical domain both by practitioners and also in the course of biomedical research. In addition to serving as labels for biomedical classes, these names reflect the organizational principles of biomedical vocabularies and ontologies. Some names represent invariant features (classes, universals) of biomedical reality (i.e., they are a matter for ontology). Other names, however, convey also how this reality is perceived, measured, (...)
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  41. Applied Ontology: An Introduction.Katherine Munn & Barry Smith (eds.) - 2008 - Frankfurt: ontos.
    Ontology is the philosophical discipline which aims to understand how things in the world are divided into categories and how these categories are related together. This is exactly what information scientists aim for in creating structured, automated representations, called 'ontologies,' for managing information in fields such as science, government, industry, and healthcare. Currently, these systems are designed in a variety of different ways, so they cannot share data with one another. They are often idiosyncratically structured, accessible only to those (...)
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  42. Constructing a lattice of Infectious Disease Ontologies from a Staphylococcus aureus isolate repository.Albert Goldfain, Lindsay G. Cowell & Barry Smith - 2012 - In Goldfain Albert, Cowell Lindsay G. & Smith Barry (eds.), Proceeedings of the Third International Conference on Biomedical Ontology (CEUR 897).
    A repository of clinically associated Staphylococcus aureus (Sa) isolates is used to semi‐automatically generate a set of application ontologies for specific subfamilies of Sa‐related disease. Each such application ontology is compatible with the Infectious Disease Ontology (IDO) and uses resources from the Open Biomedical Ontology (OBO) Foundry. The set of application ontologies forms a lattice structure beneath the IDO‐Core and IDO‐extension reference ontologies. We show how this lattice can be used to define a strategy for the (...)
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  43. Ontological realism: A methodology for coordinated evolution of scientific ontologies.Barry Smith & Werner Ceusters - 2010 - Applied ontology 5 (3):139-188.
    Since 2002 we have been testing and refining a methodology for ontology development that is now being used by multiple groups of researchers in different life science domains. Gary Merrill, in a recent paper in this journal, describes some of the reasons why this methodology has been found attractive by researchers in the biological and biomedical sciences. At the same time he assails the methodology on philosophical grounds, focusing specifically on our recommendation that ontologies developed for scientific purposes (...)
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  44. The Infectious Disease Ontology in the Age of COVID-19.Shane Babcock, Lindsay G. Cowell, John Beverley & Barry Smith - 2021 - Journal of Biomedical Semantics 12 (13).
    The Infectious Disease Ontology (IDO) is a suite of interoperable ontology modules that aims to provide coverage of all aspects of the infectious disease domain, including biomedical research, clinical care, and public health. IDO Core is designed to be a disease and pathogen neutral ontology, covering just those types of entities and relations that are relevant to infectious diseases generally. IDO Core is then extended by a collection of ontology modules focusing on specific diseases and pathogens. In this paper (...)
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  45. The Ontology of Biological and Clinical Statistics (OBCS) for standardized and reproducible statistical analysis.Jie Zheng, Marcelline R. Harris, Anna Maria Masci, Lin Yu, Alfred Hero, Barry Smith & Yongqun He - 2016 - Journal of Biomedical Semantics 7 (53).
    Statistics play a critical role in biological and clinical research. However, most reports of scientific results in the published literature make it difficult for the reader to reproduce the statistical analyses performed in achieving those results because they provide inadequate documentation of the statistical tests and algorithms applied. The Ontology of Biological and Clinical Statistics (OBCS) is put forward here as a step towards solving this problem. Terms in OBCS, including ‘data collection’, ‘data transformation in statistics’, ‘data visualization’, ‘statistical data (...)
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  46. Ontologies of cellular networks.Arp Robert & Barry Smith - 2008 - Science Signalling 1 (50):1--3.
    A comparison of six alternative definitions of the term 'cellular pathway' against the background of ontological realism.
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  47. The Plant Ontology: A common reference ontology for plants.L. Walls Ramona, D. Cooper Laurel, Elser Justin, W. Stevenson Dennis, Barry Smith, Mungall Chris, A. Gandolfo Maria & Jaiswal Pankaj - 2010 - In Walls Ramona L., Cooper Laurel D., Justin Elser, Stevenson Dennis W., Smith Barry, Chris Mungall, Gandolfo Maria A. & Pankaj Jaiswal (eds.), Proceedings of the Workshop on Bio-Ontologies, ISMB, Boston, July, 2010.
    The Plant Ontology (PO) (http://www.plantontology.org) (Jaiswal et al., 2005; Avraham et al., 2008) was designed to facilitate cross-database querying and to foster consistent use of plant-specific terminology in annotation. As new data are generated from the ever-expanding list of plant genome projects, the need for a consistent, cross-taxon vocabulary has grown. To meet this need, the PO is being expanded to represent all plants. This is the first ontology designed to encompass anatomical structures as well as growth and developmental stages (...)
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  48. The Industrial Ontologies Foundry proof-of-concept project.Evan Wallace, Dimitris Kiritsis, Barry Smith & Chris Will - 2018 - In Ilkyeong Moon, Gyu M. Lee, Jinwoo Park, Dimitris Kiritsis & Gregor von Cieminski (eds.), Advances in Production Management Systems. Smart Manufacturing for Industry 4.0. IFIP. pp. 402-409.
    The current industrial revolution is said to be driven by the digitization that exploits connected information across all aspects of manufacturing. Standards have been recognized as an important enabler. Ontology-based information standard may provide benefits not offered by current information standards. Although there have been ontologies developed in the industrial manufacturing domain, they have been fragmented and inconsistent, and little has received a standard status. With successes in developing coherent ontologies in the biological, biomedical, and financial domains, (...)
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  49. Ontologies for the study of neurological disease.Alexander P. Cox, Mark Jensen, William Duncan, Bianca Weinstock-Guttman, Kinga Szigeti, Alan Ruttenberg, Barry Smith & Alexander D. Diehl - 2012 - In Alexander P. Cox, Mark Jensen, William Duncan, Bianca Weinstock-Guttman, Kinga Szigeti, Alan Ruttenberg, Barry Smith & Alexander D. Diehl (eds.), Towards an Ontology of Mental Functioning (ICBO Workshop), Third International Conference on Biomedical Ontology. Graz:
    We have begun work on two separate but related ontologies for the study of neurological diseases. The first, the Neurological Disease Ontology (ND), is intended to provide a set of controlled, logically connected classes to describe the range of neurological diseases and their associated signs and symptoms, assessments, diagnoses, and interventions that are encountered in the course of clinical practice. ND is built as an extension of the Ontology for General Medical Sciences — a high-level candidate OBO Foundry ontology (...)
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  50. A plant disease extension of the Infectious Disease Ontology.Ramona Walls, Barry Smith, Elser Justin, Goldfain Albert, W. Stevenson Dennis & Pankaj Jaiswal - 2012 - In Walls Ramona, Smith Barry, Justin Elser, Albert Goldfain & Stevenson Dennis W. (eds.), Proceeedings of the Third International Conference on Biomedical Ontology (CEUR 897). pp. 1-5.
    Plants from a handful of species provide the primary source of food for all people, yet this source is vulnerable to multiple stressors, such as disease, drought, and nutrient deficiency. With rapid population growth and climate uncertainty, the need to produce crops that can tolerate or resist plant stressors is more crucial than ever. Traditional plant breeding methods may not be sufficient to overcome this challenge, and methods such as highOthroughput sequencing and automated scoring of phenotypes can provide significant new (...)
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