A Layered Model for Building Ontology Translation Systems
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A Survey of Top-Level Ontologies to Inform the Ontological Choices for a Foundation Data Model
A survey of Top-Level Ontologies To inform the ontological choices for a Foundation Data Model Version 1 Contents 1 Introduction and Purpose 3 F.13 FrameNet 92 2 Approach and contents 4 F.14 GFO – General Formal Ontology 94 2.1 Collect candidate top-level ontologies 4 F.15 gist 95 2.2 Develop assessment framework 4 F.16 HQDM – High Quality Data Models 97 2.3 Assessment of candidate top-level ontologies F.17 IDEAS – International Defence Enterprise against the framework 5 Architecture Specification 99 2.4 Terminological note 5 F.18 IEC 62541 100 3 Assessment framework – development basis 6 F.19 IEC 63088 100 3.1 General ontological requirements 6 F.20 ISO 12006-3 101 3.2 Overarching ontological architecture F.21 ISO 15926-2 102 framework 8 F.22 KKO: KBpedia Knowledge Ontology 103 4 Ontological commitment overview 11 F.23 KR Ontology – Knowledge Representation 4.1 General choices 11 Ontology 105 4.2 Formal structure – horizontal and vertical 14 F.24 MarineTLO: A Top-Level 4.3 Universal commitments 33 Ontology for the Marine Domain 106 5 Assessment Framework Results 37 F. 25 MIMOSA CCOM – (Common Conceptual 5.1 General choices 37 Object Model) 108 5.2 Formal structure: vertical aspects 38 F.26 OWL – Web Ontology Language 110 5.3 Formal structure: horizontal aspects 42 F.27 ProtOn – PROTo ONtology 111 5.4 Universal commitments 44 F.28 Schema.org 112 6 Summary 46 F.29 SENSUS 113 Appendix A F.30 SKOS 113 Pathway requirements for a Foundation Data F.31 SUMO 115 Model 48 F.32 TMRM/TMDM – Topic Map Reference/Data Appendix B Models 116 ISO IEC 21838-1:2019 -
Semantic Web Foundations for Representing, Reasoning, and Traversing Contextualized Knowledge Graphs
Wright State University CORE Scholar Browse all Theses and Dissertations Theses and Dissertations 2017 Semantic Web Foundations for Representing, Reasoning, and Traversing Contextualized Knowledge Graphs Vinh Thi Kim Nguyen Wright State University Follow this and additional works at: https://corescholar.libraries.wright.edu/etd_all Part of the Computer Engineering Commons, and the Computer Sciences Commons Repository Citation Nguyen, Vinh Thi Kim, "Semantic Web Foundations for Representing, Reasoning, and Traversing Contextualized Knowledge Graphs" (2017). Browse all Theses and Dissertations. 1912. https://corescholar.libraries.wright.edu/etd_all/1912 This Dissertation is brought to you for free and open access by the Theses and Dissertations at CORE Scholar. It has been accepted for inclusion in Browse all Theses and Dissertations by an authorized administrator of CORE Scholar. For more information, please contact [email protected]. Semantic Web Foundations for Representing, Reasoning, and Traversing Contextualized Knowledge Graphs A dissertation submitted in partial fulfilment of the requirements for the degree of Doctor of Philosophy By VINH THI KIM NGUYEN B.E., Vietnam National University, 2007 2017 Wright State University WRIGHT STATE UNIVERSITY GRADUATE SCHOOL December 15, 2017 I HEREBY RECOMMEND THAT THE THESIS PREPARED UNDER MY SUPERVISION BY Vinh Thi Kim Nguyen ENTITLED Semantic Web Foundations of Representing, Reasoning, and Traversing in Contextualized Knowledge Graphs BE ACCEPTED IN PARTIAL FUL- FILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF Doctor of Philosophy. Amit Sheth, Ph.D. Dissertation Director Michael Raymer, Ph.D. Director, Computer Science and Engineering Ph.D. Program Barry Milligan, Ph.D. Interim Dean of the Graduate School Committee on Final Examination Amit Sheth, Ph.D. -
Application of Ontological Models for Representing Engineering Concepts in Engineering
SCIENTIFIC PROCEEDINGS X INTERNATIONAL CONGRESS "MACHINES, TECHNOLОGIES, MATERIALS" 2013 ISSN 1310-3946 APPLICATION OF ONTOLOGICAL MODELS FOR REPRESENTING ENGINEERING CONCEPTS IN ENGINEERING Martin Ivanov1, Nikolay Tontchev2 New Bulgarian University, Sofia, Montivideo 211, University of Transport, Sofia, Geo Milev 1582 Abstract: The necessity, opportunities and advantages from developing ontological models of engineering concepts in the field of machine manufacturing technology are introduced. The characteristics and principles of the methodology for developing engineering technology models are presented. An experimental ontological model developed by the authors based on Protégé OWL 4.2 is described. Keywords: ONTOLOGICAL MODELS, ENGINEERING CONCEPTS, IDEF5, PROTEGE OWL 1. Introduction - Existing objects and phenomena in the subject area in the terms of the accepted terminology (vocabulary/vocabularies, Modern engineering activities take place in an evolving and in this case – the so called engineering lexicon or EL); enriching information environment characterized by exponentially - How objects and phenomena relate to each other; increasing information flows, variety of means for access and in the formats of data presentation, diversity of engineering problems, - How they are used within the subject area, and outside it; solved by automated means, penetration of the artificial intelligence methods and the semantic Web, too. Effectiveness of engineering is - Rules that define their existence and behavior. highly dependent on the access and the opportunities for efficient The ontology is composed on the basis of a specific vocabulary processing of this information, in particular by the use of models for and terminology characteristic for the subject area. This implies (a) formally describing, processing and dissemination of knowledge strict definitions (axioms) of the terms and rules (grammars) resulting from research and engineering practice. -
AI/ML Finding a Doing Machine
Digital Readiness: AI/ML Finding a doing machine Gregg Vesonder Stevens Institute of Technology http://vesonder.com Three Talks • Digital Readiness: AI/ML, The thinking system quest. – Artificial Intelligence and Machine Learning (AI/ML) have had a fascinating evolution from 1950 to the present. This talk sketches the main themes of AI and machine learning, tracing the evolution of the field since its beginning in the 1950s and explaining some of its main concepts. These eras are characterized as “from knowledge is power” to “data is king”. • Digital Readiness: AI/ML, Finding a doing machine. – In the last decade Machine Learning had a remarkable success record. We will review reasons for that success, review the technology, examine areas of need and explore what happened to the rest of AI, GOFAI (Good Old Fashion AI). • Digital Readiness: AI/ML, Common Sense prevails? – Will there be another AI Winter? We will explore some clues to where the current AI/ML may reunite with GOFAI (Good Old Fashioned AI) and hopefully expand the utility of both. This will include extrapolating on the necessary melding of AI with engineering, particularly systems engineering. Roadmap • Systems – Watson – CYC – NELL – Alexa, Siri, Google Home • Technologies – Semantic web – GPUs and CUDA – Back office (Hadoop) – ML Bias • Last week’s questions Winter is Coming? • First Summer: Irrational Exuberance (1948 – 1966) • First Winter (1967 – 1977) • Second Summer: Knowledge is Power (1978 – 1987) • Second Winter (1988 – 2011) • Third Summer (2012 – ?) • Why there might not be a third winter! Henry Kautz – Engelmore Lecture SYSTEMS Winter 2 Systems • Knowledge is power theme • Influence of the web, try to represent all knowledge – Creating a general ontology organizing everything in the world into a hierarchy of categories – Successful deep ontologies: Gene Ontology and CML Chemical Markup Language • Indeed extreme knowledge – CYC and Open CYC – IBM’s Watson Upper ontology of the world Russell and Norvig figure 12.1 Properties of a subject area and how they are related Ferrucci, D., et.al. -
Towards a Conceptual Framework for Persistent
Nova Southeastern University NSUWorks CEC Theses and Dissertations College of Engineering and Computing 2017 Towards a Conceptual Framework for Persistent Use: A Technical Plan to Achieve Semantic Interoperability within Electronic Health Record Systems Shellon Blackman-Lees Nova Southeastern University, [email protected] This document is a product of extensive research conducted at the Nova Southeastern University College of Engineering and Computing. For more information on research and degree programs at the NSU College of Engineering and Computing, please click here. Follow this and additional works at: https://nsuworks.nova.edu/gscis_etd Part of the Computer Sciences Commons Share Feedback About This Item NSUWorks Citation Shellon Blackman-Lees. 2017. Towards a Conceptual Framework for Persistent Use: A Technical Plan to Achieve Semantic Interoperability within Electronic Health Record Systems. Doctoral dissertation. Nova Southeastern University. Retrieved from NSUWorks, College of Engineering and Computing. (998) https://nsuworks.nova.edu/gscis_etd/998. This Dissertation is brought to you by the College of Engineering and Computing at NSUWorks. It has been accepted for inclusion in CEC Theses and Dissertations by an authorized administrator of NSUWorks. For more information, please contact [email protected]. Towards a Conceptual Framework for Persistent Use: A Technical Plan to Achieve Semantic Interoperability within Electronic Health Record Systems By Shellon M. Blackman-Lees A dissertation report submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Computer Information Systems College of Engineering and Computing Nova Southeastern University 2017 An Abstract of a Dissertation Submitted to Nova Southeastern University in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy Towards a Conceptual Framework for Persistent Use: A Technical Plan to Achieve Semantic Interoperability within Electronic Health Record Systems by Shellon M. -
Ontology and Information Systems
Ontology and Information Systems 1 Barry Smith Philosophical Ontology Ontology as a branch of philosophy is the science of what is, of the kinds and structures of objects, properties, events, processes and relations in every area of reality. ‘Ontology’ is often used by philosophers as a synonym for ‘metaphysics’ (literally: ‘what comes after the Physics’), a term which was used by early students of Aristotle to refer to what Aristotle himself called ‘first philosophy’.2 The term ‘ontology’ (or ontologia) was itself coined in 1613, independently, by two philosophers, Rudolf Göckel (Goclenius), in his Lexicon philosophicum and Jacob Lorhard (Lorhardus), in his Theatrum philosophicum. The first occurrence in English recorded by the OED appears in Bailey’s dictionary of 1721, which defines ontology as ‘an Account of being in the Abstract’. Methods and Goals of Philosophical Ontology The methods of philosophical ontology are the methods of philosophy in general. They include the development of theories of wider or narrower scope and the testing and refinement of such theories by measuring them up, either against difficult 1 This paper is based upon work supported by the National Science Foundation under Grant No. BCS-9975557 (“Ontology and Geographic Categories”) and by the Alexander von Humboldt Foundation under the auspices of its Wolfgang Paul Program. Thanks go to Thomas Bittner, Olivier Bodenreider, Anita Burgun, Charles Dement, Andrew Frank, Angelika Franzke, Wolfgang Grassl, Pierre Grenon, Nicola Guarino, Patrick Hayes, Kathleen Hornsby, Ingvar Johansson, Fritz Lehmann, Chris Menzel, Kevin Mulligan, Chris Partridge, David W. Smith, William Rapaport, Daniel von Wachter, Chris Welty and Graham White for helpful comments. -
Ontology Languages – a Review
International Journal of Computer Theory and Engineering, Vol.2, No.6, December, 2010 1793-8201 Ontology Languages – A Review V. Maniraj, Dr.R. Sivakumar 1) Logical Languages Abstract—Ontologies have been becoming a hot research • First order predicate logic topic for the application in artificial intelligence, semantic web, Software Engineering, Library Science and information • Rule based logic Architecture. Ontology is a formal representation of set of concepts within a domain and relationships between those • concepts. It is used to reason about the properties of that Description logic domain and may be used to define the domain. An ontology language is a formal language used to encode the ontologies. A 2) Frame based Languages number of research languages have been designed and released • Similar to relational databases during the past few years by the research community. They are both proprietary and standard based. In this paper a study has 3) Graph based Languages been reported on the different features and issues of these • languages. This paper also addresses the challenges for Semantic network research community in the further development of ontology languages. • Analogy with the web is rationale for the semantic web I. INTRODUCTION Ontology engineering (or ontology building) is a subfield II. BACKGROUND of knowledge engineering that studies the methods and CycL1 in computer science and artificial intelligence is an methodologies for building ontologies. It studies the ontology language used by Doug Lenat’s Cye artificial ontology development process, the ontology life cycle, the intelligence project. Ramanathan V. Guna was instrumental methods and methodologies for building ontologies, and the in the design of the language. -
An Approach to Formalizing Ontology Driven Semantic Integration: Concepts, Dimensions and Framework Wenlong Gao
Florida State University Libraries Electronic Theses, Treatises and Dissertations The Graduate School 2012 An Approach to Formalizing Ontology Driven Semantic Integration: Concepts, Dimensions and Framework Wenlong Gao Follow this and additional works at the FSU Digital Library. For more information, please contact [email protected] THE FLORIDA STATE UNIVERSITY COLLEGE OF COMMUNICATION AND INFORMATION AN APPROACH TO FORMALIZING ONTOLOGY DRIVEN SEMANTIC INTEGRATION: CONCEPTS, DIMENSIONS AND FRAMEWORK By WENLONG GAO A Dissertation submitted to the School of Library and Information Studies In partial fulfillment of the requirements for the degree of Doctor of Philosophy Degree Awarded: Spring Semester, 2012 Wenlong Gao defended this dissertation on December 9, 2011 The members of the supervisory committee were: Corinne Jörgensen Professor Directing Dissertation Daniel Schwartz University Representative Ian Douglas Committee Member Besiki Stvilia Committee Member The Graduate School has verified and approved the above-named committee members and certifies that the dissertation has been approved in accordance with university requirements. ii For my parents, I could not have done this without you. iii ACKNOWLEDGEMENTS This dissertation could not have been completed without the tremendous support and help of my dissertation committee, friends and family. First, I would like to thank Dr. Corinne Jörgensen, my dissertation chair and mentor. You supported me through this entire process, and encouraged me whenever I encountered challenges. I will always appreciate the trust and confidence you placed in me. It has been an honor and privilege to be your doctoral student. Second, I am thankful for other committee members, Dr. Ian Douglas, Dr. Besiki Stvilia, Dr. Daniel Schwartz, who inspired me not only throughout the dissertation development but also though my personal academic development. -
Semantic Web and Peer-To-Peer Steffenstaab·Heinerstuckenschmidt(Eds.) Semantic Web and Peer-To-Peer
Semantic Web and Peer-to-Peer SteffenStaab·HeinerStuckenschmidt(Eds.) Semantic Web and Peer-to-Peer Decentralized Management and Exchange of Knowledge and Information With 89 Figures and 15 Tables 123 Editors Steffen Staab University of Koblenz Institute for Computer Science Universitaetsstr. 1 56016 Koblenz, Germany [email protected] Heiner Stuckenschmidt University of Mannheim Institute for Practical Computer Science A5, 6 68159 Mannheim, Germany [email protected] Library of Congress Control Number: 2005936055 ACM Computing Classification (1998): C.2.4, H.3, I.2.4 ISBN-10 3-540-28346-3 Springer Berlin Heidelberg New York ISBN-13 978-3-540-28346-1 Springer Berlin Heidelberg New York This work is subject to copyright. All rights are reserved, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilm or in any other way, and storage in data banks. Duplication of this publication or parts thereof is permitted only under the provisions of the German Copyright Law of September 9, 1965, in its current version, and permission for use must always be obtained from Springer. Violations are liable for prosecution under the German Copyright Law. Springer is a part of Springer Science+Business Media springeronline.com © Springer-Verlag Berlin Heidelberg 2006 Printed in Germany The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant pro- tective laws and regulations and therefore free for general use. -
Which Semantics?
This item is the archived peer-reviewed author-version of: Tracing the origins of the semantic web Reference: Guns Raf.- Tracing the origins of the semantic web Journal of the American Society for Information Science and Technology / American Society for Information Science and Technology - ISSN 1532-2882 - 64:10(2013), p. 2173-2181 DOI: http://dx.doi.org/doi:10.1002/asi.22907 Handle: http://hdl.handle.net/10067/1111170151162165141 Institutional repository IRUA This paper is a postprint of the following article: Guns, R. (2013). Tracing the origins of the Semantic Web. Journal of the American Society for Information Science and Technology, 64(10), 2173–2181. http://dx.doi.org/10.1002/asi.22907 Tracing the origins of the Semantic Web Raf Guns Universiteit Antwerpen, IBW, Venusstraat 35, B-2000 Antwerp, Belgium Abstract The Semantic Web has been criticized for not being semantic. This article examines the question: why and how has the Web of Data, expressed in RDF, come to be known as the Semantic Web? Contrary to previous papers, we deliberately take a descriptive stance and do not start from preconceived ideas about the nature of semantics. Instead, we mainly base our analysis on early design documents of the (Semantic) Web. The main determining factor is shown to be link typing, coupled with the influence of online metadata. Both factors are already present in early Web standards and drafts. Our findings indicate that the Semantic Web is directly linked to older AI work, despite occasional claims to the contrary. Because of link typing, the Semantic Web can be considered an example of a semantic network. -
Relating Ontology Languages and Web Standards
Ontology Languages and Web Standards Relating Ontology Languages and Web Standards Dieter Fensel Abstract Currently computers are changing from single isolated devices to entry points in a world wide network of information exchange and business transactions called the World Wide Web (WWW). Therefore support in data, information, and knowledge exchange becomes the key issue in current computer technology. Ontologies provide a shared and common understanding of a domain that can be communicated between people and application systems. Therefore, they may play a major role in supporting information exchange processes in various areas. However, in order to develope their full power, the representation languages for ontologies must be compartible with existing data exchange standards in the World Wide Web. In this paper we investigate protoypical examples of ontology languages and web languages. Finally we show how these web standards can be used as a representation languages for ontologies. 1 Introduction Ontologies are a popular research topic in various communities such as knowledge engineering, natural language processing, cooperative information systems, intelligent information integration, and knowledge management. They provide a shared and common understanding of a domain that can be communicated between people and heterogeneous and distributed application systems. They have been developed in Artificial Intelligence to facilitate knowledge sharing and reuse. Ontologies are formal theories about a certain domain of discourse and therefore require a formal logical language to express them. The paper will discuss languages for describing ontologies in Section 2. In Section 3 we will investigate how recent web standards such as XML and RDF can be used as languages for expressing Ontologies, or at least some of their aspects. -
A Probabilistic Extension to the Web Ontology Language
A Probabilistic Extension to Ontology Language OWL Zhongli Ding and Yun Peng Department of Computer Science and Electrical Engineering University of Maryland Baltimore County Baltimore, Maryland 21250 {zding1, ypeng}@cs.umbc.edu Phone: 410-455-3816 Fax: 410-455-3969 A Probabilistic Extension to Ontology Language OWL Abstract With the development of the semantic web activity, ontologies become widely used to represent the conceptualization of a domain. However, none of the existing ontology languages provides a means to capture uncertainty about the concepts, properties and instances in a domain. Probability theory is a natural choice for dealing with uncertainty. Incorporating probability theory into existing ontology languages will provide these languages additional expressive power to quantify the degree of the overlap or inclusion between two concepts, support probabilistic queries such as finding the most probable concept that a given description belongs to, and make more accurate semantic integration possible. One approach to provide such a probabilistic extension to ontology languages is to use Bayesian networks, a widely used graphic model for knowledge representation under uncertainty. In this paper, we present our on-going research on extending OWL, an ontology language recently proposed by W3C’s Semantic Web Activity. First, the language is augmented to allow additional probabilistic markups , so probabilities can be attached with individual concepts and properties in an OWL ontology. Secondly, a set of translation rules is defined to convert this probabilistically annotated OWL ontology into a Bayesian network. Our probabilistic extension to OWL has clear semantics: the Bayesian network obtained will be associated with a joint probability distribution over the application domain.