Environments, Methodologies and Languages for Supporting Users in Building a Chemical Ontology
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A Framework for Comparing the Use of a Linguistic Ontology in an Application
A Framework for Comparing the use of a Linguistic Ontology in an Application Julieanne van Zyl1 and Dan Corbett2 Abstract. A framework recently developed for understanding and “a body of knowledge about the world (or a domain) that a) is classifying ontology applications provides opportunities to review a repository of primitive symbols used in meaning the state of the art, and to provide guidelines for application b) organises these symbols in a tangled subsumption developers from different communities [1]. The framework hierarchy; and identifies four main categories of ontology applications: neutral c) further interconnects these symbols using a rich system of authoring, ontology as specification, common access to semantic and discourse-pragmatic relations defined among information, and ontology-based search. Specific scenarios are the concepts” [2]. described for each category and a number of features have been identified to highlight the similarities and differences between There has been some work on comparing ontology applications, them. In this paper we identify additional scenarios, describing the which refer to “the application that makes use of or benefits from use of a linguistic ontology in an application. We populate the the ontology (possibly directly)” [1]. A framework [3] was scenarios with a number of prominent research and industrial developed for comparing various projects in ontology design, applications from diverse communities such as knowledge-based which considered the differences and similarities in the way the machine translation, medical databases, heterogenous information projects treat some basic knowledge representation aspects. The systems integration, multilingual text generation, Web-based projects compared include three natural language ontologies, two applications, and information retrieval. -
A Translation Approach to Portable Ontology Specifications
Knowledge Systems Laboratory September 1992 Technical Report KSL 92-71 Revised April 1993 A Translation Approach to Portable Ontology Specifications by Thomas R. Gruber Appeared in Knowledge Acquisition, 5(2):199-220, 1993. KNOWLEDGE SYSTEMS LABORATORY Computer Science Department Stanford University Stanford, California 94305 A Translation Approach to Portable Ontology Specifications Thomas R. Gruber Knowledge System Laboratory Stanford University 701 Welch Road, Building C Palo Alto, CA 94304 [email protected] Abstract To support the sharing and reuse of formally represented knowledge among AI systems, it is useful to define the common vocabulary in which shared knowledge is represented. A specification of a representational vocabulary for a shared domain of discourse — definitions of classes, relations, functions, and other objects — is called an ontology. This paper describes a mechanism for defining ontologies that are portable over representation systems. Definitions written in a standard format for predicate calculus are translated by a system called Ontolingua into specialized representations, including frame-based systems as well as relational languages. This allows researchers to share and reuse ontologies, while retaining the computational benefits of specialized implementations. We discuss how the translation approach to portability addresses several technical problems. One problem is how to accommodate the stylistic and organizational differences among representations while preserving declarative content. Another is how -
The Semantic Web and Its Languages Editor: Dieter Fensel Vrije Universiteit Amsterdam [email protected]
TRENDS & CONTROVERSIES The semantic Web and its languages Editor: Dieter Fensel Vrije Universiteit Amsterdam [email protected] The Web has drastically changed the availability of electronic informa- epistemologically rich modeling primitives, provided by the frame com- tion, but its success and exponential growth have made it increasingly munity; formal semantics and efficient reasoning support, provided by difficult to find, access, present, and maintain such information for a wide description logics; and a standard proposal for syntactical exchange nota- variety of users. In reaction to this bottleneck, many new research initia- tions, provided by the Web community. (See the essay by Frank van tives and commercial enterprises have been set up to enrich available Harmelen and Ian Horrocks.) information with machine-processable semantics. Such support is essen- Another candidate for such a Web-based ontology modeling lan- tial for bringing the Web to its full potential in areas such as knowledge guage is DAML-ONT. The DARPA Agent Markup Language is a management and electronic commerce. This semantic Web will provide major, well-funded initiative, aimed at joining the many ongoing intelligent access to heterogeneous and distributed information, enabling semantic Web efforts and focused on bringing ontologies to the Web. software products (agents) to mediate between user needs and available The DAML language inherits many aspects from OIL, and the capabili- information sources. Early steps in the direction of a semantic Web were ties of the two languages are relatively similar. Both initiatives cooper- SHOE1 and later Ontobroker,2 but now many more projects exist. ate in a Joint EU/US ad hoc Agent Markup Language Committee to Originally, the Web grew mainly around HTML, which provided a achieve a joined language proposal. -
Formalizing Knowledge by Ontologies: OWL and KIF
Formalizing Knowledge by Ontologies: OWL and KIF Marchetti Andrea, Ronzano Francesco, Tesconi Maurizio, Minutoli Salvatore CNR, IIT Department, Via Moruzzi 1, I-56124, Pisa, Italy (andrea.marchetti, francesco.ronzano, maurizio.tesconi, salvatore.minutoli)@iit.cnr.it 2 I Abstract. During the last years, the activities of knowledge formaliza- tion and sharing useful to allow for semantically enabled management of information have been attracting growing attention, expecially in dis- tributed environments like the Web. In this report, after a general introduction about the basis of knowledge abstraction and its formalization through ontologies, we briefly present a list of relevant formal languages used to represent knowledge: CycL, F- Logic, LOOM, KIF, Ontolingua, RDF(S) and OWL. Then we focus our attention on the Web Ontology Language (OWL) and the Knowledge Interchange Format (KIF). OWL is the main language used to describe and share ontologies over the Web: there are three OWL sublanguages with a growing degree of expressiveness. We describe its structure as well as the way it is used in order to reasons over asserted knowledge. Moreover we briefly present three relevant OWL ontology editors: Prot´eg´e, SWOOP and Ontotrack and two important OWL reasoners: Pellet and FACT++. KIF is mainly a standard to describe knowledge among different com- puter systems so as to facilitate its exchange. We describe the main elements of KIF syntax; we also consider Sigma, an environment for cre- ating, testing, modifying, and performing inference with KIF ontologies. We comment some meaningful example of both OWL and KIF ontologies and, in conclusion, we compare their main expresive features. -
The Role of Ontology in Information Management
UNLV Retrospective Theses & Dissertations 1-1-2003 The role of ontology in information management Renato de Freitas Marteleto University of Nevada, Las Vegas Follow this and additional works at: https://digitalscholarship.unlv.edu/rtds Repository Citation Marteleto, Renato de Freitas, "The role of ontology in information management" (2003). UNLV Retrospective Theses & Dissertations. 1494. http://dx.doi.org/10.25669/0fz6-zsye This Thesis is protected by copyright and/or related rights. It has been brought to you by Digital Scholarship@UNLV with permission from the rights-holder(s). You are free to use this Thesis in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s) directly, unless additional rights are indicated by a Creative Commons license in the record and/ or on the work itself. This Thesis has been accepted for inclusion in UNLV Retrospective Theses & Dissertations by an authorized administrator of Digital Scholarship@UNLV. For more information, please contact [email protected]. THE ROLE OF ONTOLOGY IN INFORMATION MANAGEMENT by Renato de Ereitas Marteleto Bachelor of Science Universidade Federal de Ouro Preto - Brazil 1999 A thesis submitted in partial fulfillment of the requirements for the Master of Science in Computer Science Department of Computer Science Howard R. Hughes College of Engineering Graduate College University of Nevada, Las Vegas M ay 2003 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. UMI Number: 1414535 Marteleto, Renato de Freitas All rights reserved. UMI UMI Microform 1414535 Copyright 2003 by ProQuest Information and Learning Company. -
Representing and Using Ontologies
USC-CIT Technical Report 98-01 1 Representing and Using Ontologies Kuhanandha Mahalingam and Michael N. Huhns January 1998 Center for Information Technology Department of Electrical and Computer Engineering University of South Carolina Columbia, SC, U.S.A. 29208 (803) 777-5921 or [email protected] (803) 777-8045 FAX Keywords: Ontology, Java, Distributed Information Systems Abstract This paper discusses the representation of large ontologies, especially in a graphical format. It describes a tool, developed using Java, that incorporates several abstraction mechanisms that can help manage such ontologies. Furthermore, it briefly analyzes possible point-and-click approaches that can be used to form queries on these ontologies. 1 This research was partially done under a cooperative agreement between the National Institute of Standards and Technology Advanced Technology Program (under the HIIT contract, number 70NANB5H1011) and the Healthcare Open Systems and Trials, Inc. consortium. 1. Introduction The introduction of the World Wide Web (Web) and the consequent growth of other supporting technologies that, without doubt, have driven the Internet’s widespread usage have created many new problems. The most notable problem is how to manage efficiently the volume and heterogeneity of information produced on the Web each day. Further, most of the data produced is no longer just simple text, but also consists of multimedia objects. Multimedia objects require different policies, procedures, and conventions. They originate from and reside on a variety of different operating systems, and, most importantly, are large and complex. There are many approaches for coping with these problems. One of these approaches is to use ontologies for capturing the knowledge represented by the information sources, so that it can be viewed and manipulated declaratively. -
Practical Reasoning in Probabilistic Description Logic
PRACTICAL REASONING IN PROBABILISTIC DESCRIPTION LOGIC A thesis submitted to the University of Manchester for the degree of Doctor of Philosophy in the Faculty of Engineering and Physical Sciences 2011 By Pavel Klinov School of Computer Science 2 Contents Abstract 9 Declaration 10 Copyright 11 Acknowledgments 12 1 Introduction 13 1.1 Description Logic and Ontologies . 13 1.1.1 Description Logic: From Semantic Nets to OWL 2 . 14 1.1.2 Ontologies at Work . 15 1.2 Uncertainty . 16 1.2.1 Uncertainty: Ubiquitous and Versatile . 16 1.2.2 Uncertainty vs. Vagueness . 18 1.3 P-SROIQ and Pronto Reasoner . 18 1.4 Objectives . 20 1.5 Thesis Structure . 20 2 Background and Related Work 22 2.1 Description Logic . 22 2.1.1 Syntax and Semantics . 22 2.1.2 Reasoning Problems and Complexity . 25 2.1.3 The Web Ontology Language . 26 2.2 Representation and Reasoning About Uncertainty . 26 2.2.1 Knowledge Based Model Construction . 27 2.2.2 Probabilistic Logics . 34 2.3 P-SROIQ: A Probabilistic Description Logic . 48 2.3.1 Syntax and Semantics . 48 2.3.2 Reasoning Problems . 51 2.3.3 Original Algorithms . 53 3 2.3.4 Note on Probabilistic Coherence . 57 2.4 Related Work . 58 2.4.1 Propositional PSAT Solvers . 59 2.4.2 NMPROBLOG and ContraBovemRufum . 61 3 Applications and Case Studies 62 3.1 Breast Cancer Risk Assessment Problem . 62 3.1.1 Risk Types and Assessment . 63 3.1.2 Existing Tools and Models . 65 3.1.3 The BCRA Problem and P-SROIQ ............... -
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. -
Improved Knowledge Management Through First-Order Logic in Engineering Design Ontologies" (2010)
Center for e-Design Publications Center for e-Design 5-2010 Improved knowledge management through first- order logic in engineering design ontologies Paul Witherell National Institute of Standards and Technology, [email protected] Sundar Krishnamurty University of Massachusetts Amherst, [email protected] Ian R. Grosse University of Massachusetts Amherst, [email protected] Jack C. Wileden University of Massachusetts Amherst, [email protected] Follow this and additional works at: http://lib.dr.iastate.edu/edesign_pubs Part of the Industrial Engineering Commons, and the Programming Languages and Compilers Commons Recommended Citation Witherell, Paul; Krishnamurty, Sundar; Grosse, Ian R.; and Wileden, Jack C., "Improved knowledge management through first-order logic in engineering design ontologies" (2010). Center for e-Design Publications. 1. http://lib.dr.iastate.edu/edesign_pubs/1 This Article is brought to you for free and open access by the Center for e-Design at Iowa State University Digital Repository. It has been accepted for inclusion in Center for e-Design Publications by an authorized administrator of Iowa State University Digital Repository. For more information, please contact [email protected]. Improved knowledge management through first-order logic in engineering design ontologies Abstract This paper presents the use of first-order logic to improve upon currently employed engineering design knowledge management techniques. Specifically, this work uses description logic in unison with Horn logic, to not only guide the knowledge acquisition process but also to offer much needed support in decision making during the engineering design process in a distributed environment. The knowledge management methods introduced are highlighted by the ability to identify modeling knowledge inconsistencies through the recognition of model characteristic limitations, such as those imposed by model idealizations. -
Experiments and Results on the Use of Ontologies in the Artificial Intelligence Domain
EXPERIMENTS AND RESULTS ON THE USE OF ONTOLOGIES IN THE ARTIFICIAL INTELLIGENCE DOMAIN Vasile-Daniel Păvăloaia Alexandru Ioan Cuza University of Iaşi, Romania [email protected] Abstract: The field of agent-based systems as part of the Artificial Intelligence domain is, now-a- days, quite popular. There are specialized technologies required for building software agents and it should be communicative, capable, autonomous and adaptive. In fact, these are the key characteristics required to help make the Internet activity more successful. The limiting factors in building such systems are being overcome, and new approaches are emerging from information technology research laboratories around the world. The use of ontology has proven to be essential elements in many applications and thus, they have been successfully applied in agent systems technology, knowledge management systems, and e-commerce platforms. The current research aims to present besides some theoretical aspects and examples of using the web agents for two European cities. Keywords: Ontologies, Knowledge Acquisition Systems, Artificial Intelligence, Knowledge management, intelligent agents JEL Classification: M15, M29 INTRODUCTION The world we live in is characterized by globalization, rush times, hyper competition and strong alliances (Maha, Donici and Maha, 2010; Marston, 2007). Consequently, no business can survive without emphasizing on their competitive advantages. The artificial intelligence domain holds the key for some technologies’ that can help companies to overcome the fiercely competition (Buckingham, 2011) and the web agents development is one example. There are specialized technologies that allow the development of intelligent software agents and they should be able to feature characteristics of a human being. As a result, they should be communicative, capable, autonomous and adaptive according to the particularities of the environment they activate in. -
Ontology for Information Systems (O4IS) Design Methodology Conceptualizing, Designing and Representing Domain Ontologies
Ontology for Information Systems (O4IS) Design Methodology Conceptualizing, Designing and Representing Domain Ontologies Vandana Kabilan October 2007. A Dissertation submitted to The Royal Institute of Technology in partial fullfillment of the requirements for the degree of Doctor of Technology . The Royal Institute of Technology School of Information and Communication Technology Department of Computer and Systems Sciences IV DSV Report Series No. 07–013 ISBN 978–91–7178–752–1 ISSN 1101–8526 ISRN SU–KTH/DSV/R– –07/13– –SE V All knowledge that the world has ever received comes from the mind; the infinite library of the universe is in our own mind. – Swami Vivekananda. (1863 – 1902) Indian spiritual philosopher. The whole of science is nothing more than a refinement of everyday thinking. – Albert Einstein (1879 – 1955) German-Swiss-U.S. scientist. Science is a mechanism, a way of trying to improve your knowledge of na- ture. It’s a system for testing your thoughts against the universe, and seeing whether they match. – Isaac Asimov. (1920 – 1992) Russian-U.S. science-fiction author. VII Dedicated to the three KAs of my life: Kabilan, Rithika and Kavin. IX Abstract. Globalization has opened new frontiers for business enterprises and human com- munication. There is an information explosion that necessitates huge amounts of informa- tion to be speedily processed and acted upon. Information Systems aim to facilitate human decision-making by retrieving context-sensitive information, making implicit knowledge ex- plicit and to reuse the knowledge that has already been discovered. A possible answer to meet these goals is the use of Ontology. -
Knowledge Graph Reasoning Over Unseen RDF Data
Wright State University CORE Scholar Browse all Theses and Dissertations Theses and Dissertations 2019 Knowledge Graph Reasoning over Unseen RDF Data Bhargavacharan Reddy Kaithi 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 Kaithi, Bhargavacharan Reddy, "Knowledge Graph Reasoning over Unseen RDF Data" (2019). Browse all Theses and Dissertations. 2251. https://corescholar.libraries.wright.edu/etd_all/2251 This Thesis 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]. Knowledge Graph Reasoning over Unseen RDF Data A Thesis submitted in partial fulfillment of the requirements for the degree of Master of Science by BHARGAVACHARAN REDDY KAITHI B.Tech., CVR College of Engineering and Technology, Jawaharlal Nehru Technological University, India, 2017 2019 Wright State University Wright State University GRADUATE SCHOOL August 29th, 2019 I HEREBY RECOMMEND THAT THE THESIS PREPARED UNDER MY SUPER- VISION BY Bhargavacharan Reddy Kaithi ENTITLED Knowledge Graph Reasoning over Unseen RDF Data BE ACCEPTED IN PARTIAL FULFILLMENT OF THE RE- QUIREMENTS FOR THE DEGREE OF Master of Science. Pascal Hitzler, Ph.D. Thesis Director Mateen M. Rizki, Ph.D. Chair, Department of Computer Science and Engineering Committee on Final Examination Pascal Hitzler, Ph.D. Mateen M. Rizki, Ph.D. Yong Pei, Ph.D. Barry Milligan, Ph.D. Interim Dean of the Graduate School ABSTRACT Kaithi, Bhargavacharan Reddy.