College of Computing Sciences and Information Technology (CCSIT),Teerthanker Mahaveer University, Moradabad. Web Ontology

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1 Abstract The original idea of the Sementic Web was to bring machine-readable descriptions to the data and documents already on the Web, in order to improve search and data usage. Pages are written in HTML (Hyper Text Markup Language), a language that is useful for publishing information intended only for human consumption. The Sementic Web aims at defining ways to allow Web information to be used by computers not only for display purposes, but also for interoperability and integration between systems and applications. One way to enable machineto-machine exchange and automated processing is to provide the information in such a way that computers can understand it. Keywords: Semantic Web, OWL(ontology web language), Ontology, Ontology language. I. INTRODUCTION The OWL Web Ontology Language is an international standard for encoding and exchanging ontologies and is designed to support the Semantic Web. The concept of the Semantic Web is that information should be given explicit meaning, so that machines can process it more intelligently. Instead of just creating standard terms for concepts as is done in XML, the Semantic Web also allows users to provide formal definitions for the standard terms they create. Machines can then use inference algorithms to reason about the terms. For example, a semantic web search engine may conclude that a particular CD-read/write drive matches a query for Storage Devices under $100. Furthermore, if two different sets of terms are in turn defined using a third set of common terms, and then it is possible to automatically perform (partial) translations between them. It is envisioned that the Semantic Web will enable more intelligent search, electronic personal assistants, more efficient e-commerce, and coordination of heterogeneous embedded systems. Web Ontology Ankit Kumar Mishra 1, Mahendra Singh Sagar 2 College of Computing Sciences and Information Technology Teerthanker Mahaveer University, Moradabad India 1ankitmishra988@gmail.com 2mahendra.singh12jan@gmail.com types, properties, and interrelationships of the entities in a domain of discourse. The entities are conceptualizations (limited abstractions) of phenomena. Ontology compartmentalizes the variables needed for some set of computations, and establishes the relationships between them. The fields of artificial intelligence, the Sementic Web, systems engineering, software engineering, biomedical informatics, library science, enterprise bookmarking, and information architecture all create ontologies to limit complexity and to organize information. The ontology can then be applied to problem solving. III. COMPONENTS OF ONTOLOGY Common components of ontologies include: 3.1 Individuals: Instances or objects (the basic or "ground level" objects) 3.2 Classes: Sets, collections, concepts, classes in programming, types of objects, or kinds of things. 3.3 Attributes: Aspects, properties, features, characteristics, or parameters that objects (and classes) can have. 3.4 Relations: II. ONTOLOGY In computer science and information science, ontology is a formal framework for representing knowledge. This framework names and defines the Ways in which classes and individuals can be related to one another. 3.5 Function terms: 261

2 Complex structures formed from certain relations that can be used in place of an individual term in a statement. 3.6 Restrictions: Formally stated descriptions of what must be true in order for some assertion to be accepted as input. 3.7 Rules: Statements in the form of an if-then (antecedentconsequent) sentence that describe the logical inferences that can be drawn from an assertion in a particular form. 3.8 Axioms: Assertions (including rules) in a logical form that together comprise the overall theory that the ontology describes in its domain of application. This definition differs from that of "axioms" in generative grammar and formal logic. In those disciplines, axioms include only statements asserted as a priori knowledge. As used here, "axioms" also include the theory derived from axiomatic statements 3.9 Events: The changing of attributes or relations. IV. 4.1 Domain ontology TYPES OF ONTOLOGY A domain ontology (or domain-specific ontology) represents concepts which belong to part of the world. Particular meanings of terms applied to that domain are provided by domain ontology. For example the word card has many different meanings. An ontology about the domain of poker would model the "playing card" meaning of the word, while an ontology about the domain of computer hardware would model the "punched card" and "video card" meanings. Since domain ontologies represent concepts in very specific and often eclectic ways, they are often incompatible. As systems that rely on domain ontologies expand, they often need to merge domain ontologies into a more general representation. This presents a challenge to the ontology designer. Different ontologies in the same domain arise due to different languages, different intended usage of the ontologies, and different perceptions of the domain (based on cultural background, education, ideology, etc.). At present, merging ontologies that are not developed from a common foundation ontology is a largely manual process and therefore timeconsuming and expensive. Domain ontologies that use the same foundation ontology to provide a set of basic elements with which to specify the meanings of the domain ontology elements can be merged automatically. There are studies on generalized techniques for merging ontologies,[12] but this area of research is still largely theoretical. 4.2 Upper ontology An upper ontology (or foundation ontology) is a model of the common objects that are generally applicable across a wide range of domain ontologies. It usually employs a core glossary that contains the terms and associated object descriptions as they are used in various relevant domain sets. There are several standardized upper ontologies available for use, including BFO, Dublin Core, GFO, Open Cyc/Research Cyc, SUMO, the Unified Foundational Ontology (UFO), and DOLCE. WorldNet, while considered an upper ontology by some, is not strictly ontology. However, it has been employed as a linguistic tool for learning domain ontologies. 4.3 Hybrid ontology The Gellish ontology is an example of a combination of an upper and domain ontology. 262

3 V. 5-CHALLENGES FOR A NEW SEMANTIC WORLD As with every technological evolution, the Sementic Web and ontologies need to promote their unique value proposition for specific target groups in order to achieve adoption. A common pitfall made in the studies of the Sementic Web is the limited focus on technological perspectives or, in the other extreme, the difficulty to communicate the underlying capacity of Semantics and ontologies to meet critical real world challenges. An interesting starting point for analysis, which also justifies the contribution of this edition, relates with some of the characteristics of our world and society. 5.1 Globalization Creation and consumption of knowledge and information are made in the global context. From this perspective, the elimination of local boundaries and the exploitation of synergies and capacities beyond boundaries require advanced adoption mechanisms that permit realization of opportunities, deep understanding of threats and strategic fit to human and social networks towards new levels of performance. 5.2 Networking In our era, business and economic activities, as well as competition, require new models of business networking. Within this context, advanced documentation of skills, competencies, business models and context based collaboration define new demands for advanced business and social networking at a global level. 5.3 Shared models A global consensus towards peace, development, prosperity and a better world needs to be based on shared conceptual models that define the average common understanding of human societies for the issues that matters at a global scale. And while this can be perceived either as a too optimistic scenario, or as a wishful thinking case, in the global information landscape shared models are required for interoperability, exploitation of synergies and definition of new milestones for collective intelligence. 5.4 Collective intelligence: The increased capacities of networking as a result of globalization and widespread adoption of shared models has resulted in the development of a global trend to apply collective intelligence filters or collaborative filtering in the context of the global information world. Such development challenges many traditional models of business performance, marketing and profitability. VI. ONTOLOGY BUILDING TOOLS In this section, with reference to a Survey published on XML site that covers Software Tools that have Ontology editing capabilities and are in use today. These ontology building tools (I.e. Protégé 3.4, IsaViz, SWOOP and Apollo) may be useful for building ontology schemas (terminological component) alone or together with instance data. Concise descriptions of each software tool were compiled and then reviewed by the organization currently providing the software for commercial, open, or restricted distribution. The descriptions are factored into a dozen different categories covering important functions and features of the software. These categories are summarizing the results. We have used only Four popular and accepted ontology authoring tools (Apollo, Protégé 3.4, IsaViz and SWOOP), taking into consideration the advantages of these tools. Tools that provide support for the different phases of the ontology engineering process are referred to as ontology building tools. Ontology Scope Ontology Capture Ontology Encoding Ontology Integration Ontology Evaluation Ontology Documentation FIG.1 ONTOLOGY CONSTRUCTION METHODOLOGY 263

4 6.1 PROTÉGÉ 3.4 Protégé, is an ontology and knowledge base editor produced by Stanford University. Protégé is a tool that enables the construction of domain ontologies, customized data entry forms to enter data. Protégé allows the definition of classes, class hierarchies, variables, variable-value limits, and the relationships between classes and the properties of these relationships. Protégé is free and can be downloaded from Protégé comes with visualization packages such as OntoViz, EZPal, etc.; all of these help the user visualize ontologies with the help of diagrams. Stanford University is doing a magnificent job of continually improving Protégé. As part of its last update, Protégé now includes an interface for SWRL (Semantic Web Rule Language), which sits on top of OWL to do math, temporal reasoning, and adds Prolong-type reasoning rules. Stanford has a tutorial that covers the basics of using Protégé with the OWL plug-in. 6.2 ISAVIZ IsaViz is a visual environment for browsing and authoring RDF models as graphs. This tool is offered by W3C Consortium. IsaViz was developed by Emmanuel Pietriga.The first version was developed in collaboration with Xerox Research Centre Europe which also contributed with XVTM, the ancestor of ZVTM (Zoom able Visual Transformation Machine) upon which IsaViz is built. As of October 2004, further developments are handled by INRIA Futures project In Situ. 6.3 APOLLO Apollo is a user-friendly information modeling application. The modeling is based around the basic primitives, such as classes, instances, functions, relations etc. Internal model is build as a frame system according to the internal model of the OKBC protocol. Apollo currently does not support non template class slots. For each class is possible to create a number of instances. An instance inherits all spaces of the class. Each slot has a set of facets. 6.4 SWOOP SWOOP is a Web-based OWL ontology editor and browser [4]. SWOOP contains OWL validation and offers various OWL presentation syntax views. It has reasoning support and provides a multiple ontology environment. Ontologies can be compared, edited and merged. Different ontologies can be compared against their Description Logicbased definitions, associated properties and instances. SWOOP s interface has hyperlinked capabilities so that navigation can be simple and easy. SWOOP does not follow a methodology for ontology construction. Users can reuse external ontological data. This is possible either by purely linking to the external entity, or importing the entire external ontology. It is not possible to do partial imports of OWL. There are several ways to achieve this, such as a brute-force syntactic scheme to copy/paste relevant parts (axioms) of the external ontology, or a more elegant solution that involves partitioning the external ontology while preserving its semantics and then reusing (importing) only the specific partition as desired. VII. SEMETIC WEB LAYER CAKE TRUST LOGIC ONTOLOGY RDF SCHEMA RDF XML SCHEMA XML 264

5 Fig.2-The layered technologies of the Semantic web according to the TIM BERNER LEE and the World Wide Web Consortium (W3C),Each layer is seen as building on-and requiring-the once below it. The W3C has developed or, is in the process of developing, standards and recommendations for all but the top two layers and the W3C recommendation for digital signature and managing encryption keys will also play roles in the trust layer. XML- Extensible Markup Language. The language framework that, since 1998 has been used to define nearly all languages that are used to interchange data over web. XML SCHEMA- A language used to define the structure of xml languages. RDF- Resource Description Framework, a flexible language capable of describing all sorts of information and Meta data. RDF SCHEMA- A frame work that provides a means to specify basic vocabularies for specific RDF application languages to use. ONTOLOGY- Languages used to define vocabularies and establish the usage of words and terms in context of a specific vocabulary. RDF SCHEMA is a framework for constructing ontologies and is used by many more advanced ontology framework. OWL is an ontology language designed for the Semantic web. LOGIC AND PROOF- Logical reasoning is used to establish the consistency and correctness of datasets and to infer conclusions that are not explicitly stated but are required by consistent with a known set of data. Proofs trace or explain the steps of logical reasoning. TRUST- A means of providing authentication of identity and evidence of the trustworthiness of data, services and agents. VIII. LANGUAGES FOR ONTOLOGY An ontology language is a formal language used to encode the ontology. There are a number of such languages for ontologies, both proprietary and standards-based: Common Algebraic Specification Language is a general logic-based specification language developed within the IFIP working group 1.3 "Foundations of System Specifications" and functions as a de facto standard in the area of software specifications. It is now being applied to ontology specifications in order to provide modularity and structuring mechanisms. Common logic is ISO standard 24707, a specification for a family of ontology languages that can be accurately translated into each other. The Cyc project has its own ontology language called CycL, based on first-order predicate calculus with some higher-order extensions. DOGMA (Developing Ontology-Grounded Methods and Applications) adopts the factoriented modeling approach to provide a higher level of Sementic stability. The Gellish language includes rules for its own extension and thus integrates an ontology with an ontology language. IDEF5 is a software engineering method to develop and maintain usable, accurate, domain ontologies. 265

6 KIF is a syntax for first-order logic that is based on S-expressions. MOF and UML are standards of the OMG OBO, a language used for biological and biomedical ontologies. Onto UML is an ontologically well-founded profile of UML for conceptual modeling of domain ontologies. OWL is a language for making ontological statements, developed as a follow-on from RDF and RDFS, as well as earlier ontology language projects including OIL, DAML, and DAML+OIL. OWL is intended to be used over the World Wide Web, and all its elements (classes, properties and individuals) are defined as RDF resources, and identified by URIs. To play its role of describing data and Meta data, RDF (and other potential languages for the role) needs to include the following capabilities: Able to describe most kind of data that will be able Able to describe the structural design for data sets Able to describe the relation between bits of data Rule Interchange Format (RIF) and F- Logic combine ontologies and rules. Semantic Application Design Language (SADL) captures a subset of the expressiveness of OWL, using an Englishlike language entered via an Eclipse Plug-in. SBVR (Semantics of Business Vocabularies and Rules) is an OMG standard adopted in industry to build ontologies. TOVE Project, Toronto Virtual Enterprise project RDF (Resource Description Framework) RDF is designed for specific data about specific subject. OWL (WEB ONTOLOGY LANGUAGE) Sometimes it is enough to sketch out several classes and properties, more often that s only the beginning. You find that you need to restrict the cardinality (a car may have no more than four wheels), to express optionality (a car may have a cd player),to combine classes(participants are member of both the High School and of the town s marching band),or in myriad other ways be more precise about the design of your ontology. OWL (which stands for Web Ontology Languagethe acronym are out of order but nevertheless, the owl it is) is a W3C project to standardize a more capable ontology framework than RDFS. OWL evolved from DAML+OIL,a relatively successful ontology project in DARPA, the UNITED STATE DEFENCE ADVANCED RESEARCH PROJECTS AGENCY. In fact many of the same people who 266

7 helped to DAML (as it is known for short) have been working on OWL. OWL has just been released as final recommendation; much DAML work is migrated to OWL. 1-Flavours of OWL- OWL FULL- OWL full is the complete. It allows an RDF data store, (a collection of RDF statements),to be complex enough to give a logical reasoned serious trouble. OWL DL- OWL DL supports a form of what is called description logic. Description logics apply certain carefully chosen restrictions to the kind of things that can be said in order to gain computing advantages. This allows you to be sure that description logic process complete and decidable. OWL LITE- OWL LITE is the OWL DL with more restrictions. The idea is to make it easy to start with and easy to implement processor so the people can being use OWL LITE easily and later graduate to more complicated uses. 2-OWL background- OWL s design has benefitted from several generations of earlier ontology languages, a strong theoretical basis and a determination on the part of many of its designers to create a language suitable for use on the Semantic web. IX. THE IMPORTANCE OF SEMENTICS FOR ORGANIZATIONS Today, integration is a top priority for many European and worldwide enterprises. The European community alone is investing, through the Seventh Framework Program, more that 200 million on research involving inter-enterprise interoperability and Semantics. Most organizations have already realized that the use of Semantic Web technologies is a promising candidate solution to support crossorganizational cooperation for SME (Small and Medium-sized Enterprises) that operate in dynamically changing Work environments. Semantic Web technologies are more and more considered as a key technology to resolve the problems of interoperability and integration within the heterogeneous world of ubiquitously interconnected systems with respect to the nature of components, standards, Data formats, protocols, etc. Moreover, we also believe that Sementic Web technologies can facilitate not only the discovery of heterogeneous components and data integration, but also the communication between individuals. Sementic inter-enterprise interoperability is the key for the implementation of the idea of a knowledgebased economy where networks of enterprises (and SME in particular) can gain advantages from the peculiarities of the productive European fabric (many small companies, highly specialized, closely connected with each other, highly flexible thanks to the diffusion of knowledge and very skilled people in the whole fabric). The world-class competitiveness of enterprises strongly depends, in the future, on their ability to rapidly set-up, and maintains, virtual, networked enterprise structures. Novel technologies for interoperability within and between enterprises need to emerge to radically solve a problem that has not been sufficiently addressed by the research community before. In fact, managing the Semantics of business-tobusiness interaction may be the most Challenging task in integrated e-business value chains, and there is more and more evidence that Semantic Web technology has the potential to actually mitigate such problems. X. ASSESSMENT AND ANALYSIS The results for comparison of tools are shown in the form of Tables which are categorized on the basis of Tool architecture: in which Extensibility and ontology storage are closely examined. Tool s Interoperability: in that Import Format, Export Format and Merging features are discussed. Tools inference services which include Inference Engine, Exception Handling and Consistency 267

8 Checking.Tools usability that discussed Collaboration with other tools, Ontology Library and Visualization. Overview of Tools' versioning and collaborative work support on the basis of Versioning and collaboration. An important aspect when analysing a tool is its tool architecture (Table 10.1).We have included information about extensibility and storage of the ontologies (databases, ACII files, etc.). From this perspective, most of the tools are moving towards extensible architectures. Storage in databases is still a weak point of ontology tools, since just a few of them i.e. Protégé 3.4 use databases for storing ontologies. Interoperability (Table 10.2) with other ontology development tools, merging tools, information systems and databases, as well as translations to and from some ontology languages, is another important feature in order to integrate ontologies in applications. Most of the new tools export and import to ad-hoc XML and other markup languages. However, there is not a comparative study about the quality of all these translators. Moreover, there are no empirical results about the possibility of exchanging ontologies between different tools and about the loose of knowledge in the translation processes. This includes: built-in and other inference engines, consistency checking mechanisms and exception handling, among others. Protégé-3.4 performs inference using PAL. Finally, none of the tools provide exception-handling mechanisms. Related to the usability of tools, Protégé 3.4 has the most advanced features related to the cooperative and collaborative construction of ontologies. In general, more features are required in existing tools to ensure the successful collaborative building of ontologies. Finally, other usability aspects related to help system, edition & visualization, etc., should be improved in most of the tools. Feature Apollo IsaViz Protégé SWOOP 3.4 Extensibility No NO Via NO Ontology Storage Plud-ins Files Files Files& DBMS No Feature Apollo IsaViz Protégé 3.4 Import OCML Format Export Format OCML XSLT, RDF (S), OIL, DAML+OI L, OWL XSLT, RDF (S), OIL, DAML+OI L, OWL TABLE 10.2: TOOLS' INTEROPERABILITY TABLE 10.3: OVERVIEW OF TOOLS' VERSIONING AND COLLABORATIVE WORK Via type inheritance and detection of cycles in hierarchies SUPPORT CONCLUSION XML, RDF (S), XML Schema and OWL XML, RDF (S), XML Schema, Java, html Merging No No Via ANCHOR PROMPT plug-in Feature Apollo IsaViz Protégé 3.4 Inference Engine Exception Handling Consistency Checking No Yes With PAL SWOO P RDF (S), OIL, DAML, RDF (S), OIL, DAML, No Protégé 3.4 We have looked at ontologies and how they establish organized sets of concepts and vocabularies. Simpler ontology may take the form of hierarchical list, but in many real world applications more complex network of concepts are needed. To be useful for Semantic web, an ontology language must do more than just define a vocabularies and place constrains on the on the use of terms. It must be: No No No No No Yes Via plug ins like FACT and PA Only checks writing mistakes TABLE10.1 TOOLS,S ARCHITECTURE 268

9 Able to reference concepts defined elsewhere on the web Sharable over the web Able to work with one or more languages in use(like RDF) Able to merge several ontologies Widely accepted as a standard Expressive enough for serious use REFERENCES [1] Jump up^ "XML and Semantic Web W3C Standards Timeline". [2] Jump up^ W3C working group [3] Jump up^ "Submission Request to W3C: OWL 1.1 Web Ontology Language". W3C Jump up^ "OWL 2 Web Ontology Language Document Overview". W3C [4] [5] ^ Jump up to: a b "W3C Standard Facilitates Data Management and Integration". W3.org Retrieved 15 October [6] Jump up^ Sirin, E.; Parsia, B.; Grau, B. C.; Kalyanpur, A.; Katz, Y. (2007). "Pellet: A practical OWL-DL reasoner". Web Semantics: Science, Services and Agents on the World Wide Web 5 (2): doi: /j.websem edit [7] Jump up^ Pellet [8] Jump up^ RacerPro [9] Jump up^ Tsarkov, D.; Horrocks, I. (2006). "FaCT++ Description Logic Reasoner: System Description". "Automated Reasoning". Lecture Notes in Computer Science pp doi: / _26. ISBN edit [10] 10 Aditya Kalyanpur, Bijan Parsia, Evren Sirin, Bernardo Cuenca Grau, James A. Hendler: Swoop: A Web Ontology Editing Browser. J. Web Sem. 4(2): (2006). [11] [12] 11 [13] 12 International journal of Web & Semantic Technology (IJWesT) Vol.1, Num.3, July

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