OIL: an Ontology Infrastructure for the Semantic Web

Total Page:16

File Type:pdf, Size:1020Kb

Load more

The Semantic Web OIL: An Ontology Infrastructure for the Semantic Web Dieter Fensel and Frank van Harmelen, Vrije Universiteit, Amsterdam Ian Horrocks, University of Manchester, UK Deborah L. McGuinness, Stanford University Peter F. Patel-Schneider, Bell Laboratories esearchers in artificial intelligence first developed ontologies to facilitate knowl- R edge sharing and reuse. Since the beginning of the 1990s, ontologies have become a popular research topic, and several AI research communities—including Ontologies play a knowledge engineering, natural language processing, and knowledge representation— major role in have investigated them. More recently, the notion of application programs have direct access to data an ontology is becoming widespread in fields such semantics. The resource description framework2 supporting information as intelligent information integration, cooperative has taken an important additional step by defining information systems, information retrieval, elec- a syntactical convention and a simple data model exchange across tronic commerce, and knowledge management. for representing machine-processable data seman- Ontologies are becoming popular largely because of tics. RDF is a standard for the Web metadata the various networks. A what they promise: a shared and common under- World Wide Web Consortium (www.w3c.org/rdf) standing that reaches across people and application develops, and it defines a data model based on prerequisite for such a systems. triples: object, property, and value. The RDF Currently, ontologies applied to the World Wide Schema3 takes a step further into a richer represen- role is the development Web are creating the Semantic Web.1 Originally, the tation formalism and introduces basic ontological Web grew mainly around HTML, which provides a modeling primitives into the Web. With RDFS, we of a joint standard for standard for structuring documents that browsers can can talk about classes, subclasses, subproperties, translate in a canonical way to render those docu- domain and range restrictions of properties, and so specifying and ments. On the one hand, HTML’s simplicity helped forth in a Web-based context. We took RDFS as a spur the Web’s fast growth; on the other, its simplic- starting point and enriched it into a full-fledged exchanging ontologies. ity seriously hampered more advanced Web appli- Web-based ontology language called OIL.4 We cations in many domains and for many tasks. This included these aspects: The authors present led to XML (see Figure 1), which lets developers define arbitrary domain- and task-specific extensions • A more intuitive choice of some of the modeling OIL, a proposal for (even HTML appears as an XML application— primitives and richer ways to define concepts and XHTML). attributes. such a standard. XML is basically a defined way to provide a seri- • The definition of a formal semantics for OIL. alized syntax for tree structures—it is an important • The development of customized editors and infer- first step toward building a Semantic Web, where ence engines to work with OIL. 38 1094-7167/01/$10.00 © 2001 IEEE IEEE INTELLIGENT SYSTEMS Ontologies: A revolution tion spread over different sources. for information access and • Maintaining: Sustaining weakly struc- DAML-O OIL integration tured text sources is difficult and time- Many definitions of ontologies have sur- consuming when such sources become RDFS faced in the last decade, but the one that in large. Keeping such collections consis- our opinion best characterizes an ontology’s tent, correct, and up to date requires a XHTML RDF essence is this: “An ontology is a formal, mechanized representation of semantics HTML XML explicit specification of a shared conceptual- and constraints that can help detect ization.”5 In this context, conceptualization anomalies. refers to an abstract model of some phenom- • Automatic document generation: Adaptive Figure 1. The layer language model for enon in the world that identifies that phe- Web sites that enable dynamic reconfigu- the Web. nomenon’s relevant concepts. Explicit means ration according to user profiles or other that the type of concepts used and the con- relevant aspects could prove very useful. tomer as an instant market overview. Their straints on their use are explicitly defined, and The generation of semistructured infor- functionality is provided through wrappers, formal means that the ontology should be mation presentations from semistructured which use keyword search to find product machine understandable. Different degrees data requires a machine-accessible repre- information together with assumptions on of formality are possible. Large ontologies sentation of the semantics of these infor- regularities in presentation format and text such as WordNet (www.cogsci.princeton. mation sources. extraction heuristics. This technology has edu/~wn) provide a thesaurus for over two severe limitations: 100,000 terms explained in natural language. Using ontologies, semantic annotations On the other end of the spectrum is CYC will allow structural and semantic definitions • Effort: Writing a wrapper for each online (www.cyc.com), which provides formal of documents. These annotations could pro- store is time-consuming, and changes in axiomating theories for many aspects of com- vide completely new possibilities: intelligent store presentation or organization increase monsense knowledge. Shared reflects the search instead of keyword matching, query maintenance. notion that an ontology captures consensual answering instead of information retrieval, • Quality: The extracted product informa- knowledge—that is, it is not restricted to document exchange between departments tion is limited (it contains mostly price some individual but is accepted by a group. through ontology mappings, and definitions information), error-prone, and incomplete. The three main application areas of ontol- of views on documents. For example, a wrapper might extract the ogy technology are knowledge management, product price, but it usually misses indi- Web commerce, and electronic business. Web commerce rect costs such as shipping. E-commerce is an important and growing Knowledge management business area for two reasons. First, e-com- Most product information is provided in KM is concerned with acquiring, maintain- merce extends existing business models—it natural language; automatic text recognition ing, and accessing an organization’s knowl- reduces costs, extends existing distribution is still a research area with significant edge. Its purpose is to exploit an organization’s channels, and might even introduce new dis- unsolved problems. However, the situation intellectual assets for greater productivity, new tribution possibilities. Second, it enables com- will drastically change in the near future value, and increased competitiveness. Owing pletely new business models and gives them when standard representation formalisms for to globalization and the Internet’s impact, a much greater importance than they had data structure and semantics are available. many organizations are increasingly geo- before. What has up to now been a peripheral Software agents will then understand prod- graphically dispersed and organized around aspect of a business field can suddenly receive uct information. Meta online stores will grow virtual teams. With the large number of online its own important revenue flow. with little effort, which will enable complete documents, several document management Examples of business field extensions are market transparency in the various dimen- systems have entered the market. However, online stores; examples of new business sions of the diverse product properties. these systems have weaknesses: fields are shopping agents and online mar- Ontology mappings, which translate different ketplaces and auction houses that turn com- product descriptions, will replace the low- • Searching information: Existing keyword- parison shopping into a business with its level programming of wrappers, which is based searches retrieve irrelevant informa- own significant revenue flow. The advan- based on text extraction and format heuris- tion that uses a certain word in a different tages of online stores and their success sto- tics. An ontology will describe the various context; they might miss information when ries have led to a large number of shopping products and help navigate and search auto- different words about the desired content pages. The new task for customers is to find matically for the required information. are used. a shop that sells the product they’re seek- • Extracting information: Current human ing, getting it in the desired quality, quan- Electronic business browsing and reading requires extracting tity, and time, and paying as little as possi- E-commerce in the business-to-business relevant information from information ble for it. Achieving these goals through field (B2B) is not new—initiatives to sup- sources. Automatic agents lack the com- browsing requires significant time and only port it in business processes between dif- monsense knowledge required to extract covers a small share of the actual offers. ferent companies existed in the 1960s. To such information from textual representa- Shopbots visit several stores, extract prod- exchange business transactions electroni- tions, and they fail to integrate informa- uct information, and present it to the cus- cally, sender and receiver must agree on a MARCH/APRIL 2001 computer.org/intelligent 39 The Semantic Web an
Recommended publications
  • Bi-Directional Transformation Between Normalized Systems Elements and Domain Ontologies in OWL

    Bi-Directional Transformation Between Normalized Systems Elements and Domain Ontologies in OWL

    Bi-directional Transformation between Normalized Systems Elements and Domain Ontologies in OWL Marek Suchanek´ 1 a, Herwig Mannaert2, Peter Uhnak´ 3 b and Robert Pergl1 c 1Faculty of Information Technology, Czech Technical University in Prague, Thakurova´ 9, Prague, Czech Republic 2Normalized Systems Institute, University of Antwerp, Prinsstraat 13, Antwerp, Belgium 3NSX bvba, Wetenschapspark Universiteit Antwerpen, Galileilaan 15, 2845 Niel, Belgium Keywords: Ontology, Normalized Systems, Transformation, Model-driven Development, Ontology Engineering, Software Modelling. Abstract: Knowledge representation in OWL ontologies gained a lot of popularity with the development of Big Data, Artificial Intelligence, Semantic Web, and Linked Open Data. OWL ontologies are very versatile, and there are many tools for analysis, design, documentation, and mapping. They can capture concepts and categories, their properties and relations. Normalized Systems (NS) provide a way of code generation from a model of so-called NS Elements resulting in an information system with proven evolvability. The model used in NS contains domain-specific knowledge that can be represented in an OWL ontology. This work clarifies the potential advantages of having OWL representation of the NS model, discusses the design of a bi-directional transformation between NS models and domain ontologies in OWL, and describes its implementation. It shows how the resulting ontology enables further work on the analytical level and leverages the system design. Moreover, due to the fact that NS metamodel is metacircular, the transformation can generate ontology of NS metamodel itself. It is expected that the results of this work will help with the design of larger real-world applications as well as the metamodel and that the transformation tool will be further extended with additional features which we proposed.
  • Ontojit: Parsing Native OWL DL Into Executable Ontologies in an Object Oriented Paradigm

    Ontojit: Parsing Native OWL DL Into Executable Ontologies in an Object Oriented Paradigm

    OntoJIT: Parsing Native OWL DL into Executable Ontologies in an Object Oriented Paradigm Sohaila Baset and Kilian Stoffel Information Management Institute University of Neuchatel, Neuchatel, Switzerland [email protected], [email protected] Abstract. Despite meriting the growing consensus between researchers and practitioners of ontology modeling, the Web Ontology Language OWL still has a modest presence in the communities of "traditional" web developers and software engineers. This resulted in hoarding the se- mantic web field in a rather small circle of people with a certain profile of expertise. In this paper we present OntoJIT, our novel approach to- ward a democratized semantic web where we bring OWL ontologies into the comfort-zone of end-application developers. We focus particularly on parsing OWL source files into executable ontologies in an object oriented programming paradigm. We finally demonstrate the dynamic code-base created as the result of parsing some reference OWL DL ontologies. Keywords: Ontologies, OWL, Semantic Web, Meta Programming,Dynamic Compilation 1 Background and Motivation With a stack full of recognized standards and specifications, the Web Ontology Language OWL has made long strides to allocate itself a distinctive spot in the landscape of knowledge representation and semantic web. Obviously, OWL is not the only player in the scene; over the couple of last decades many other languages have also emerged in the ontology modeling paradigm. Most of these languages are logic-based formalisms with underlying constructs in first order logic [5][7][8][11] or in one of the description logic fragments like OWL itself [3][4] and its predecessor DAML+OIL [10].
  • Open Geospatial Consortium

    Open Geospatial Consortium

    Open Geospatial Consortium Submission Date: 2016-08-23 Approval Date: 2016-11-29 Publication Date: 2016-01-31 External identifier of this OGC® document: http://www.opengis.net/doc/geosciml/4.1 Internal reference number of this OGC® document: 16-008 Version: 4.1 Category: OGC® Implementation Standard Editor: GeoSciML Modelling Team OGC Geoscience Markup Language 4.1 (GeoSciML) Copyright notice Copyright © 2017 Open Geospatial Consortium To obtain additional rights of use, visit http://www.opengeospatial.org/legal/. IUGS Commission for the Management and Application of Geoscience Information, Copyright © 2016-2017. All rights reserved. Warning This formatted document is not an official OGC Standard. The normative version is posted at: http://docs.opengeospatial.org/is/16-008/16-008.html This document is distributed for review and comment. This document is subject to change without notice and may not be referred to as an OGC Standard. Recipients of this document are invited to submit, with their comments, notification of any relevant patent rights of which they are aware and to provide supporting documentation. Document type: OGC® Standard Document subtype: Document stage: Approved for Publis Release Document language: English i Copyright © 2016 Open Geospatial Consortium License Agreement Permission is hereby granted by the Open Geospatial Consortium, ("Licensor"), free of charge and subject to the terms set forth below, to any person obtaining a copy of this Intellectual Property and any associated documentation, to deal in the Intellectual Property without restriction (except as set forth below), including without limitation the rights to implement, use, copy, modify, merge, publish, distribute, and/or sublicense copies of the Intellectual Property, and to permit persons to whom the Intellectual Property is furnished to do so, provided that all copyright notices on the intellectual property are retained intact and that each person to whom the Intellectual Property is furnished agrees to the terms of this Agreement.
  • Ontology Definition Metamodel

    Ontology Definition Metamodel

    Date: May 2009 Ontology Definition Metamodel Version 1.0 OMG Document Number: formal/2009-05-01 Standard document URL: http://www.omg.org/spec/ODM/1.0 Associated Files*: http://www.omg.org/spec/ODM/20080901 http://www.omg.org/spec/ODM/20080902 * source files: ptc/2008-09-09 (CMOF XMI), ptc/2008-09-09 (UML2 XMI) Copyright © 2005-2008, IBM Copyright © 2009, Object Management Group, Inc. Copyright © 2005-2008, Sandpiper Software, Inc. USE OF SPECIFICATION - TERMS, CONDITIONS & NOTICES The material in this document details an Object Management Group specification in accordance with the terms, conditions and notices set forth below. This document does not represent a commitment to implement any portion of this specification in any company's products. The information contained in this document is subject to change without notice. LICENSES The companies listed above have granted to the Object Management Group, Inc. (OMG) a nonexclusive, royalty-free, paid up, worldwide license to copy and distribute this document and to modify this document and distribute copies of the modified version. Each of the copyright holders listed above has agreed that no person shall be deemed to have infringed the copyright in the included material of any such copyright holder by reason of having used the specification set forth herein or having conformed any computer software to the specification. Subject to all of the terms and conditions below, the owners of the copyright in this specification hereby grant you a fully-paid up, non-exclusive, nontransferable, perpetual,
  • A Detailed Comparison of UML and OWL

    A Detailed Comparison of UML and OWL

    REIHE INFORMATIK TR-2008-004 A Detailed Comparison of UML and OWL Kilian Kiko und Colin Atkinson University of Mannheim – Fakultät für Mathematik und Informatik – Lehrstuhl für Softwaretechnik A5, 6 D-68159 Mannheim, Germany 1 A Detailed Comparison of UML and OWL Kilian Kiko University of Mannheim Colin Atkinson University of Mannheim Abstract As models and ontologies assume an increasingly central role in software and information systems engineering, the question of how exactly they compare and how they can sensibly be used together assumes growing importance. However, no study to date has systematically and comprehensively compared the two technology spaces, and a large variety of different bridging and integration ideas have been proposed in recent years without any detailed analysis of whether they are sound or useful. In this paper, we address this problem by providing a detailed and comprehensive comparison of the two technology spaces in terms of their flagship languages – UML and OWL – each a de facto and de jure standard in its respective space. To fully analyze the end user experience, we perform the comparison at two levels – one considering the underlying boundary assumptions and philosophy adopted by each language and the other considering their detailed features. We also consider all relevant auxiliary languages such as OCL. The resulting comparison clarifies the relationship between the two technologies and provides a solid foundation for deciding how to use them together or integrate them. 2 Index Terms – Analysis, language comparison, representation languages, ontology languages, software modeling languages, syntax, semantics, interpretation assumptions. 1 Introduction A major trend in IT over the last decade has been the move towards greater inter-connectivity of systems and the creation of enterprise-wide computing solutions.
  • Ontology Definition Metamodel

    Ontology Definition Metamodel

    Date: September 2014 Ontology Definition Metamodel Version 1.1 OMG Document Number: formal/2014-09-02 Standard Document URL: http://www.omg.org/spec/ODM/1.1/ Normative Machine Consumable Files: http://www.omg.org/spec/ODM/20131101/ODM-metamodels.xmi http://www.omg.org/spec/ODM/20131101/RDFProfile.xmi http://www.omg.org/spec/ODM/20131101/OWLProfile.xmi http://www.omg.org/spec/ODM/20131101/RDFLibrary.xmi http://www.omg.org/spec/ODM/20131101/XSDLibrary.xmi http://www.omg.org/spec/ODM/20131101/OWLLibrary.xmi Copyright © 2009-2014, 88Solutions Copyright © 2013-2014, ACORD Copyright © 2009-2014, Adaptive, Inc. Copyright © 2009-2014, California Institute of Technology. United States Government sponsorship acknowledged. Copyright © 2005-2012, Computer Sciences Corporation Copyright © 2009-2014, Deere & Company Copyright © 2005-2014, International Business Machines Corporation Copyright © 2013-2014, Institute for Defense Analyses Copyright © 2009-2014, Object Management Group, Inc. Copyright © 2011-2014, No Magic, Inc. Copyright © 2005-2012, Raytheon Company Copyright © 2005-2011, Sandpiper Software, Inc. Copyright © 2009-2014, Sparx Systems Pty Ltd Copyright © 2011-2014, Thematix Partners LLC USE OF SPECIFICATION - TERMS, CONDITIONS & NOTICES The material in this document details an Object Management Group specification in accordance with the terms, conditions and notices set forth below. This document does not represent a commitment to implement any portion of this specification in any company's products. The information contained in this document is subject to change without notice. LICENSES The companies listed above have granted to the Object Management Group, Inc. (OMG) a nonexclusive, royalty-free, paid up, worldwide license to copy and distribute this document and to modify this document and distribute copies of the modified version.
  • Experience in Reasoning with the Foundational Model of Anatomy in Owl Dl

    Experience in Reasoning with the Foundational Model of Anatomy in Owl Dl

    Pacific Symposium on Biocomputing 2006: World Scientific; 2006. p. 200-211. ´' 2006 World Scientific Publishing Co. Pte. Ltd. EXPERIENCE IN REASONING WITH THE FOUNDATIONAL MODEL OF ANATOMY IN OWL DL SONGMAO ZHANG 1, OLIVIER BODENREIDER 2, CHRISTINE GOLBREICH 3 1 Institute of Mathematics, Academy of Mathematics and System Sciences, Chinese Academy of Sciences, Beijing, China {[email protected]} 2 U.S. National Library of Medicine, National Institutes of Health, Bethesda, MD, USA {[email protected]} 3 LIM, University Rennes 1, Rennes, France {[email protected]} The objective of this study is to compare description logics (DLs) and frames for representing large-scale biomedical ontologies and reasoning with them. The ontology under investigation is the Foundational Model of Anatomy (FMA). We converted it from its frame-based representation in Protégé into OWL DL. The OWL reasoner Racer helped identify unsatisfiable classes in the FMA. Support for consistency checking is clearly an advantage of using DLs rather than frames. The in- terest of reclassification was limited, due to the difficulty of defining necessary and sufficient con- ditions for anatomical entities. The sheer size and complexity of the FMA was also an issue. 1 Introduction As virtually all other biomedical ontologies relate to them, reference ontologies for core domains such as anatomical entities and small molecules form the backbone of the Se- mantic Web for Life Sciences. One such ontology is the Foundational Model of Anat- omy (FMA). However, existing reference ontologies sometimes need to be adapted to Semantic Web technologies before they can actually contribute to the Semantic Web.
  • A Method for Converting Frame Based Ontologies Into OWL

    A Method for Converting Frame Based Ontologies Into OWL

    Proceedings of the Eighth International Protégé Conference 2005:108-111. Migrating the FMA from Protégé to OWL Christine Golbreich1, Songmao Zhang2, Olivier Bodenreider3 1LIM, University Rennes 1, France E-mail: [email protected] 2Institute of Mathematics, Chinese Academy of Sciences, Beijing 100080, P.R.China E-mail: [email protected] 3U.S. National Library of Medicine, 8600 Rockville Pike, MS 43, Bethesda, Maryland 20894, USA E-mail: [email protected] Abstract. This paper focuses on the migration of the Foundational Model of Anatomy from its frame-based representation in Protégé to its logical representation in OWL. First, it considers specificities of the FMA in Protégé that were taken into account for the migration, and presents some conversion rules defined for migrating FMA from Protégé 2.1 to OWL DL. Then, the incremental approach currently adopted is outlined. Preliminary results are reported, exhibiting the benefits of this work both for the FMA and for description logic systems. 1. Introduction The long term goal of this project is to provide a service assisting the conversion of frame-based ontologies to OWL, in order to take advantage of the higher expressiveness and powerful reasoning services of its underly- ing description logic (DL). Converting frame-based ontologies to OWL becomes an important issue correspond- ing to general needs for interoperability and resources sharing on the Semantic Web. This trend is already ob- served in medicine, where biomedical thesauri are currently being migrated to OWL (e.g. Gene Ontology, MeSH, NCI Thesaurus). The frame-based ontology under study is the Foundational Model of Anatomy (FMA, version 1.1), which was converted from Protégé 2.1 to OWL DL.
  • The Resource Description Framework and Its Schema Fabien Gandon, Reto Krummenacher, Sung-Kook Han, Ioan Toma

    The Resource Description Framework and Its Schema Fabien Gandon, Reto Krummenacher, Sung-Kook Han, Ioan Toma

    The Resource Description Framework and its Schema Fabien Gandon, Reto Krummenacher, Sung-Kook Han, Ioan Toma To cite this version: Fabien Gandon, Reto Krummenacher, Sung-Kook Han, Ioan Toma. The Resource Description Frame- work and its Schema. Handbook of Semantic Web Technologies, 2011, 978-3-540-92912-3. hal- 01171045 HAL Id: hal-01171045 https://hal.inria.fr/hal-01171045 Submitted on 2 Jul 2015 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. The Resource Description Framework and its Schema Fabien L. Gandon, INRIA Sophia Antipolis Reto Krummenacher, STI Innsbruck Sung-Kook Han, STI Innsbruck Ioan Toma, STI Innsbruck 1. Abstract RDF is a framework to publish statements on the web about anything. It allows anyone to describe resources, in particular Web resources, such as the author, creation date, subject, and copyright of an image. Any information portal or data-based web site can be interested in using the graph model of RDF to open its silos of data about persons, documents, events, products, services, places etc. RDF reuses the web approach to identify resources (URI) and to allow one to explicitly represent any relationship between two resources.
  • A Semantic Web Primer Antoniou and Van Harmelen Computer Science / Internet

    A Semantic Web Primer Antoniou and Van Harmelen Computer Science / Internet

    A Semantic Web Primer A Semantic Web computer science / Internet A Semantic Web Primer Second Edition Second Edition Grigoris Antoniou and Frank van Harmelen The development of the Semantic Web, with machine-readable content, has the potential to revolutionize the World Wide Web and its uses. A Semantic Web Primer provides an introduction and guide to this still emerging field, describing its key ideas, languages, and technologies. Suitable for use as a textbook or for self-study by professionals, it concentrates on undergraduate-level fundamental concepts and techniques that will enable readers to proceed with building applications on their own and includes exercises, project descriptions, and annotated references to relevant online materials. A Semantic Web Primer provides a systematic treatment of the different languages (XML, RDF, OWL, and rules) and technologies (explicit metadata, ontologies, and logic and inference) that are central to Semantic Web development as well as such crucial related topics as ontology engineering and application scenarios. This substantially revised and updated second edition reflects recent developments in the field, covering new application areas and tools. The new material includes a Harmelen and van Antoniou discussion of such topics as SPARQL as the RDF query language; OWL DLP and its interesting practical and theoretical Edition Second properties; the SWRL language (in the chapter on rules); OWL-S (on which the discussion of Web services is now based). The new final chapter considers the state of the art of the field today, captures ongoing discussions, and outlines the most challenging issues facing the Semantic Web in the future. Supplementary materials, including slides, online versions of many of the code fragments in the book, and links to further reading, can be found at http://www.semanticwebprimer.org.
  • Knowledge Graph Considered Harmful for Ontology

    Knowledge Graph Considered Harmful for Ontology

    Knowledge Graph Considered Harmful for Ontology Seiji Koide1 Ontolonomy, LLC., Minami-ku Yokohama 232-0066, Japan, [email protected], WWW home page: http://ontolonomy.co.jp/ Abstract. The time of knowledge graph has come. Linked Data is now rephrased to knowledge graph, and no one has doubt on the future of it. However, will the time of ontology come in the next step? There exists a long history of ontology ever since the ancient Greece, and now the terminology has become popular with the advent of OWL. Nevertheless, the ontology does not seem to happen so as Linked Data did. There is a serious gap on the semantic representation between the ontology and the knowledge graph, and the gap originates semantic networks. In this paper, we pursue the history of knowledge representation, point out the serious semantic gap contained in knowledge graph. We propose an al- ternative representation language for ontological knowledge in Semantic Webs. Keywords: New KM, RDF, OWL, knowledge graph, knowledge repre- sentation, Frame-based, Case-based 1 Introduction The purpose of this essay is to cause a stir in the community of Semantics Webs just as Dijkstra did in software engineering.1 It seems that the time of Linked Open Data (LOD) has come. Ones have become to rephrase \Linked Data" to \knowledge graph", since knowledge graph has been used as technical terminology in the domain of semantic search by Google, IBM, etc.. Because the both is roughly the same technology from the technical viewpoint. On the other hand, the term of \ontology" exists ever since the age of ancient Greece, and the engineering of ontologies has been pursued since 1970s as a part of computer science and Artificial Intelligence, then it has become popular today with OWL.
  • From SHIQ and RDF to OWL: the Making of a Web Ontology Language

    From SHIQ and RDF to OWL: the Making of a Web Ontology Language

    From SHIQ and RDF to OWL: The Making of a Web Ontology Language Ian Horrocks,1 Peter F. Patel-Schneider,2 and Frank van Harmelen3 1 Department of Computer Science University of Manchester Oxford Road, Manchester M13 9PL, UK Email: [email protected] 2 Bell Labs Research Lucent Technologies 600 Mountain Avenue, Murray Hill, New Jersey 07974 U.S.A. Email: [email protected] 3 AI Department Vrije Universiteit Amsterdam de Boelelaan 1081a, 1081HV Amsterdam, The Netherlands Email: [email protected] Abstract. The OWL Web Ontology Language is a new formal language for representing ontologies in the Semantic Web. OWL has features from several families of representation languages, including primarily Descrip- tion Logics and frames. OWL also shares many characteristics with RDF, the W3C base of the Semantic Web. In this paper we discuss how the philosophy and features of OWL can be traced back to these older for- malisms, with modifications driven by several other constraints on OWL. Several interesting problems have arisen where these influences on OWL have clashed. Keywords: Ontologies, Semantic Web, Description Logics, Frames, RDF. 1 Introduction OWL [10] is a new ontology language for the Semantic Web, developed by the World Wide Web Consortium (W3C) Web Ontology Working Group. OWL was primarily designed to represent information about categories of objects and how objects are interrelated—the sort of information that is often called an ontology. OWL can also represent information about the objects themselves—the sort of information that is often thought of as data. OWL was not designed in a vacuum.