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  1. A First-Order Logic Formalization of the Industrial Ontology Foundry Signature Using Basic Formal Ontology.Barry Smith, Farhad Ameri, Hyunmin Cheong, Dimitris Kiritsis, Dusan Sormaz, Chris Will & J. Neil Otte - 2019 - In Proceedings of the Joint Ontology Workshops (JOWO), Graz.
    Basic Formal Ontology (BFO) is a top-level ontology used in hundreds of active projects in scientific and other domains. BFO has been selected to serve as top-level ontology in the Industrial Ontologies Foundry (IOF), an initiative to create a suite of ontologies to support digital manufacturing on the part of representatives from a number of branches of the advanced manufacturing industries. We here present a first draft set of axioms and definitions of an IOF upper ontology descending from BFO. The (...)
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  2. 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, an effort called (...)
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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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