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  1. Robert Arp, Barry Smith & Andrew D. Spear (2015). Building Ontologies with Basic Formal Ontology. 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 of (...)
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  2. 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). The Ontology for Biomedical Investigations. 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 as adding semantic expressivity to (...)
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  3. Anthony F. Beavers (2011). Noesis and the Encyclopedic Internet Vision. Synthese 182 (2):315 - 333.
    Noesis is an Internet search engine dedicated to mapping the profession of philosophy online. In this paper, I recount the history of the project's development since 1998 and discuss the role it may play in representing philosophy optimally, adequately, fairly, and accessibly. Unlike many other representations of philosophy, Noesis is dynamic in the sense that it constantly changes and inclusive in the sense that it lets the profession speak for itself about what philosophy is, how it is practiced, and why (...)
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  4. Stefano Borgo, Jean-Rémi Bourguet & Adrien Barton (eds.) (2016). CEUR Workshop Proceedings of The Joint Ontology Workshops, with the 9th International Conference of Formal Ontology for Information Systems (FOIS), Early Career Symposium. CEUR Scientific Workshops.
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  5. Stefano Canali (2016). Big Data, Epistemology and Causality: Knowledge in and Knowledge Out in EXPOsOMICS. Big Data and Society 3 (2).
    Recently, it has been argued that the use of Big Data transforms the sciences, making data-driven research possible and studying causality redundant. In this paper, I focus on the claim on causal knowledge by examining the Big Data project EXPOsOMICS, whose research is funded by the European Commission and considered capable of improving our understanding of the relation between exposure and disease. While EXPOsOMICS may seem the perfect exemplification of the data-driven view, I show how causal knowledge is necessary for (...)
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  6. Werner Ceusters, Barry Smith & James Matthew Fielding (2004). LinkSuite™: Software Tools for Formally Robust Ontology-Based Data and Information Integration. In Proceedings of DILS 2004 (Data Integration in the Life Sciences), (Lecture Notes in Bioinformatics, 2994). Springer.
    The integration of information resources in the life sciences is one of the most challenging problems facing bioinformatics today. We describe how Language and Computing nv, originally a developer of ontology-based natural language understanding systems for the healthcare domain, is developing a framework for the integration of structured data with unstructured information contained in natural language texts. L&C’s LinkSuite™ combines the flexibility of a modular software architecture with an ontology based on rigorous philosophical and logical principles that is designed to (...)
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  7. Kouji Kozaki, Yuki Yamagata, Hiroko Kou, Takeshi Imai, Kazuhiko Ohe & Riichiro Mizoguchi (2014). Publishing Linked Open Data From a Disease Ontology Toward a Knowledge Infrastructure. Transactions of the Japanese Society for Artificial Intelligence 29 (4):396-405.
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  8. Vincent C. Müller (2000). How Do We Read a Dictionary (as Machines and as Humans)? Kinds of Information in Dictionaries Constructed and Reconstructed. In Evangelos Dermatas (ed.), Proceedings of COMLEX2000: Computational lexicography. Patras University Press. pp. 141-144.
    Two large lexicological projects for the Center for the Greek Language, Thessaloniki, were to be published in print and on the WWW, which meant that two conversions were needed: a near-database file had to be converted to fully formatted file for printing and a fully formatted file had to be converted to a database for WWW access. As it turned out, both conversions could make use of existing clues that indicated the kinds of information contained in each particular piece of (...)
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  9. Robert J. Rovetto, Formal Representations of Orbits.
  10. Robert J. Rovetto (2016 Sept). The Orbital Space Environment and Space Situational Awareness Domain Ontology – Towards an International Information System for Space Data. In Proceedings of The Advanced Maui Optical and Space Surveillance Technologies (AMOS) Conference.
    The orbital space environment is home to natural and artificial satellites, debris, and space weather phenomena. As the population of orbital objects grows so do the potential hazards to astronauts, space infrastructure and spaceflight capability. Orbital debris, in particular, is a universal concern. This and other hazards can be minimized by improving global space situational awareness (SSA). By sharing more data and increasing observational coverage of the space environment we stand to achieve that goal, thereby making spaceflight safer and expanding (...)
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  11. Robert J. Rovetto (2016). Orbital Space Environment and Space Situational Awareness Domain Ontology. In Stefano Borgo, Jean-Rémi Bourguet & Adrien Barton (eds.), CEUR workshop proceedings of The Joint Ontology Workshops, with the 9th International Conference of Formal Ontology for Information Systems (FOIS), Early Career Symposium. CEUR Scientific Workshops.
    A short summary paper of my Orbital Space Domain Ontology project (purl.org/space-ontology), originally conceived in 2011. Since then I've sought (without success) opportunities to realize it (either as a PhD or other degree thesis; or in an employment position) toward my original passion of entering the space sector and gaining further space education. Since then persons in the relevant space disciplines have seen the potential in it, and unfortunately some have taken advantage of my ideas yet excluded me from work. (...)
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  12. Robert J. Rovetto (2016). The Space Object Ontology. 2016 1.
    This paper develops the ontology of space objects for theoretical and computational ontology applied to the space (astronautical/astronomical) domain. It follows “An ontological architecture for Orbital Debris Data” (Rovetto, 2015) and “Preliminaries of a Space Situational Awareness Ontology” (Rovetto, Kelso, 2016). Important considerations for developing a space object ontology, or more broadly, a space domain ontology are presented. The main category term ‘Space Object’ is analyzed from a philosophical perspective. The ontological commitments of legal definitions for artificial space objects are (...)
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  13. Robert J. Rovetto (2015). An Ontological Architecture for Orbital Debris Data. Earth Science Informatics:1-16.
    The orbital debris problem presents an opportunity for inter-agency and international cooperation toward the mutually beneficial goals of debris prevention, mitigation, remediation, and improved space situational awareness (SSA). Achieving these goals requires sharing orbital debris and other SSA data. Toward this, I present an ontological architecture for the orbital debris and broader SSA domain, taking steps in the creation of an orbital debris ontology (ODO). The purpose of this ontological system is to (I) represent general orbital debris and SSA domain (...)
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  14. Robert J. Rovetto & T. S. Kelso (2016 Feb). Preliminaries of a Space Situational Awareness Ontology. In Renato Zanetti, Ryan P. Russell, Martin T. Oximek & Angela L. Bowes (eds.), Proceedings of AAS/AIAA Spaceflight Mechanics Meeting, in Advances in the Astronautical Sciences. Univelt Inc.. pp. 4177-4192.
    Space situational awareness (SSA) is vital for international safety and security, and for the future of space travel. The sharing of SSA data and information should improve the state of global SSA for planetary defense and spaceflight safety. I take steps toward a Space Situational Awareness (SSA) Ontology, and outline some central objectives, requirements and desiderata in the ontology development process for this domain. The purpose of this ontological system is to explore the potential for the ontology research topic to (...)
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  15. Robert J. Rovetto & Riichiro Mizoguchi (2015). Causality and the Ontology of Disease. Applied Ontology 10 (2):79-105.
    The goal of this paper is two-fold: first, to emphasize causality in disease ontology and knowledge representation, presenting a general and cursory discussion of causality and causal chains; and second, to clarify and develop the River Flow Model of Diseases (RFM). The RFM is an ontological account of disease, representing the causal structure of pathology. It applies general knowledge of causality using the concept of causal chains. The river analogy of disease is explained, formal descriptions are offered, and the RFM (...)
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  16. Robert John Rovetto (2016). Ontology Archtecures for the Orbital Space Environment and Space Situational Awareness Domain. In Stefano Borgo, Loris Bozzato, Chiara Del Vescovo & Martin Homola (eds.), Proceedings of the Joint Ontology Workshops with the 9th International Conference on Formal Ontology in Information Systems. CEUR.
    This paper applies some ontology architectures to the space domain, specifically the orbital and near-earth space environment and the space situational awareness domain. I briefly summarize local, single and hybrid ontology architectures, and offer potential space ontology architectures for each by showing how actual space data sources and space organizations would be involved.
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  17. Jonathan Simon (2005). Formal Ontology for Natural Language Processing and the Integration of Biomedical Databases. International Journal of Medical Informatics 75:224-231.
    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 of (...)
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  18. Barry Smith (2006). Against Idiosyncrasy in Ontology Development. In Formal Ontology in Information Systems (FOIS).
    The world of ontology development is full of mysteries. Recently, ISO Standard 15926 (“Lifecycle Integration of Process Plant Data Including Oil and Gas Production Facilities”), a data model initially designed to support the integration and handover of large engineering artefacts, has been proposed by its principal custodian for general use as an upper level ontology. As we shall discover, ISO 15926 is, when examined in light of this proposal, marked by a series of quite astonishing defects, which may however provide (...)
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  19. Barry Smith & Werner Ceusters (2006). HL7 RIM: An Incoherent Standard. Studies in Health Technology and Informatics 124:133–138.
    The Health Level 7 Reference Information Model (HL7 RIM) is lauded by its authors as ‘the foundation of healthcare interoperability’. Yet even after some 10 years of development work, the RIM is still subject to a variety of logical and ontological flaws, which has placed severe obstacles in the way of those who are called upon to develop implementations. We offer evidence that these obstacles are insurmountable and that the time has come to abandon an unworkable paradigm.
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  20. Chris F. Taylor, Dawn Field, Susanna-Assunta Sansone, Jan Aerts, Rolf Apweiler, Michael Ashburner, Catherine A. Ball, Pierre-Alain Binz, Molly Bogue, Tim Booth, Alvis Brazma, Ryan R. Brinkman, Adam Michael Clark, Eric W. Deutsch, Oliver Fiehn, Jennifer Fostel, Peter Ghazal, Frank Gibson, Tanya Gray, Graeme Grimes, John M. Hancock, Nigel W. Hardy, Henning Hermjakob, Randall K. Julian, Matthew Kane, Carsten Kettner, Christopher Kinsinger, Eugene Kolker, Martin Kuiper, Nicolas Le Novere, Jim Leebens-Mack, Suzanna E. Lewis, Phillip Lord, Ann-Marie Mallon, Nishanth Marthandan, Hiroshi Masuya, Ruth McNally, Alexander Mehrle, Norman Morrison, Sandra Orchard, John Quackenbush, James M. Reecy, Donald G. Robertson, Philippe Rocca-Serra, Henry Rodriguez, Heiko Rosenfelder, Javier Santoyo-Lopez, Richard H. Scheuermann, Daniel Schober, Barry Smith & Jason Snape (2008). Promoting Coherent Minimum Reporting Guidelines for Biological and Biomedical Investigations: The MIBBI Project. Nature Biotechnology 26 (8):889-896.
    Throughout the biological and biomedical sciences there is a growing need for, prescriptive ‘minimum information’ (MI) checklists specifying the key information to include when reporting experimental results are beginning to find favor with experimentalists, analysts, publishers and funders alike. Such checklists aim to ensure that methods, data, analyses and results are described to a level sufficient to support the unambiguous interpretation, sophisticated search, reanalysis and experimental corroboration and reuse of data sets, facilitating the extraction of maximum value from data sets (...)
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