Bio-Ontologies (#bioontologies; http://www.bio-ontologies.org.uk/) is an ISMB Community of Special Interest (COSI) that covers the latest and most innovative research in the application of ontologies and more generally the organisation, presentation and dissemination of knowledge in biomedicine and the life sciences. Join our mailing list and our slack team! Have something to say? Tweet to @bioontologies or send an email to the organizers - firstname.lastname@example.org .
Bio-Ontologies will be in its 23nd year at ISMB 2020 which will be held as a ONLINE CONFERENCE between July 12-16, 2020. Bio-ontologies strives to provide a vibrant environment for reporting novel methods and sharing experiences on the construction and application of ontologies in health care and the life sciences.
The COSI offers a constructive environment to nurture discussion of innovative and scientifically sound work that range from preliminary to completed, from both young and experienced investigators alike. Bio-Ontologies participants also benefit from a strongly interdisciplinary setting, where ISMB attendees intermingle with members from American Medical Informatics Association (AMIA) and the W3C’s Healthcare and Life Sciences Community Group (HCLSCG), thereby increasing impact through broader dissemination and enabling new and exceptional collaborations. This year's event will be integrated into the main conference and will feature papers accepted for the ISMB proceedings.
Program is available at ISMB: https://www.iscb.org/cms_addon/conferences/ismb2020/tracks/bio-ontcosi
COVID-SEE: Enabling scientific evidence exploration through semantics in a time of crisis
Karin Verspoor, University of Melbourne
The rapid increases in scientific knowledge have never been more obvious than in the wake of the emergence of the COVID-19 virus, where we have seen hundreds of new research articles being published every week as scientists and medical researchers rush to share knowledge about predicting disease spread, and management or treatment of the disease. This has left scientists scrambling to navigate and synthesise large amounts of information. We have been developing a system we call COVID-SEE (Scientific Evidence Explorer) that leverages natural language processing methods to structure key information in COVID-19-related literature, and facilitates navigation of the literature through a relational lens. I will introduce our approach, and discuss the many ways ontologies enable and support the project.
Bio: Karin Verspoor is a Professor in the School of Computing and Information Systems and Deputy Director of the Health and Biomedical Informatics Centre at the University of Melbourne. Trained as a computational linguist, Karin’s current research primarily focuses on extracting information from clinical texts and the biomedical literature using machine learning methods to enable biological discovery and clinical decision support. Karin held previous posts as the Scientific Director of Health and Life Sciences at NICTA Victoria Research Laboratory, at the University of Colorado School of Medicine, and at Los Alamos National Laboratory.
Michael Gruninger, University of Toronto
Although a plethora of ontologies have been developed in a wide variety of domains, there is often a sense in which it is difficult to measure progress in the field of applied ontology. In some domains there is a mindset that treats ontologies as being as arbitrary as software code, so there is no point in evaluating them, and there cannot possibly be any consensus on which ontologies to use. In other domains, there is an abundance of ontologies but no understanding of their relationships, leading to a perception of continually reinventing the wheel. Far too often, the only criteria for selecting ontologies are political, not technical. If we proceed further down this road, we ultimately risk irrelevance. Against this viewpoint, I would offer an approach to ontology design that focuses on formalizing the intended semantics of an ontology, so that sharability and reusability is guaranteed.
Bio: Michael Grüninger is a Professor in the Department of Industrial Engineering at the University of Toronto. Michael received his Ph.D. and M.Sc. in Computer Science at the University of Toronto and his B.Sc. in Computer Science at the University of Alberta. He was a Senior Research Scientist in the Enterprise Integration Laboratory of the Department of Mechanical and Industrial Engineering at the University of Toronto. He has developed the Process Specification Language, which is now published as an International Standard (ISO 18629). His current research focuses on the design and formal characterization of theories in mathematical logic and their application to problems in manufacturing and enterprise engineering.