J. Nagaraju, K. Ravi Kumar / International Journal of Engineering Research and                    Applications (IJERA) ISS...
J. Nagaraju, K. Ravi Kumar / International Journal of Engineering Research and                    Applications (IJERA) ISS...
J. Nagaraju, K. Ravi Kumar / International Journal of Engineering Research and                    Applications (IJERA) ISS...
J. Nagaraju, K. Ravi Kumar / International Journal of Engineering Research and                    Applications (IJERA) ISS...
J. Nagaraju, K. Ravi Kumar / International Journal of Engineering Research and                    Applications (IJERA) ISS...
J. Nagaraju, K. Ravi Kumar / International Journal of Engineering Research and                    Applications (IJERA) ISS...
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  1. 1. J. Nagaraju, K. Ravi Kumar / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 3, Issue 2, March -April 2013, pp.611-616An Ontology for Describing Web Services Through WSDL and RDF J. Nagaraju*, K. Ravi Kumar** *(Department of Computer Science,Anurag Engineering College, Kodad,Andhra Pradesh,India) **((Department of Computer Science,Anurag Engineering College ,Kodad,Andhra Pradesh,India)ABSTRACT Now a days web has become an naturally fall.[5]During the second half of the 20thimportant resource for knowledge.To provide century, philosophers extensively debated themany services to the users to acquire data easily possible methods or approaches to buildingand efficiently web services are being described ontologies without actually building any veryusing ontologies which provide a set of rules to be elaborate ontologies themselves.used easily and understood preferably.Themostly used languages used for describing web By contrast, computer scientists were buildingservices is WSDL.In this paper we have some large and robust ontologies, such as WordNetdescribed about WSDL and RDF using graphs to and Cyc, with comparatively little debate over howdescribe ontologies. they were built.Since the mid-1970s, researchers inKeywords-ontology, WSDL ,RDF, web services the field of artificial intelligence (AI) have recognized that capturing knowledge is the key to building large and powerful AI systems. AI I. Introduction researchers argued that they could create new In computer science and information ontologies as computational models that enablescience, an ontology formally represents knowledge certain kinds of automated reasoning. In the 1980s,as a set of concepts within a domain, and the the AI community began to use the term ontology torelationships between pairs of concepts. It can be refer to both a theory of a modeled world and aused to model a domain and support reasoning about component of knowledge systems.entities .In theory, an ontology is a "formal, explicitspecification of a shared conceptualisation". [1] An Some researchers, drawing inspiration fromontology renders shared vocabulary and taxonomy philosophical ontologies, viewed computationalwhich models a domain with the definition of ontology as a kind of applied philosophy. [6]In theobjects and/or concepts and their properties and early 1990s, the widely cited Web page and paperrelations.[2]Ontologies are the structural frameworks "Toward Principles for the Design of Ontologiesfor organizing information and are used in artificial Used for Knowledge Sharing" by Tom Gruber[7] isintelligence, the Semantic Web, systems credited with a deliberate definition of ontology as aengineering, software engineering, biomedical technical term in computer science. Gruberinformatics, library science, enterprise introduced the term to mean a specification of abookmarking, and information architecture as a conceptualization:"An ontology is a descriptionform of knowledge representation about the world (like a formal specification of a program) of theor some part of it. concepts and relationships that can formally exist for an agent or a community of agents.The creation of domain ontologies is alsofundamental to the definition and use of an This definition is consistent with the usage ofenterprise architecture framework.Historically, ontology as set of concept definitions, but moreontologies arise out of the branch of philosophy general. And it is a different sense of the word thanknown as metaphysics, which deals with the nature its use in philosophy.According to Gruberof reality – of what exists. This fundamental branch (1993):"Ontologies are often equated withis concerned with analyzing various types or modes taxonomic hierarchies of classes, class definitions,of existence, often with special attention to the and the subsumption relation, but ontologies needrelations between particulars and universals, not be limited to these forms. Ontologies are alsobetween intrinsic and extrinsic properties, and not limited to conservative definitions — that is,between essence and existence. The traditional goal definitions in the traditional logic sense that onlyof ontological inquiry in particular is to divide the introduce terminology and do not add anyworld "at its joints" to discover those fundamental knowledge about the world.[9] To specify acategories or kinds into which the world’s objects 611 | P a g e
  2. 2. J. Nagaraju, K. Ravi Kumar / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 3, Issue 2, March -April 2013, pp.611-616conceptualization, one needs to state axioms that do Thus,we might speak of an ontology for ―liquids‖ orconstrain the possible interpretations for the defined for ―parts and wholes.‖ Here, the singular termterms. stands for the entire set of concepts and terms needed to speak about phenomena involving liquids II. Ontology As Vocabulary and parts and wholes. When different theorists make In philosophy, ontology is the study of the different proposals for an ontology or when wekinds of things that exist. It is often said that speak about ontology proposals for differentontologies ―carve the world at its joints.‖ In AI, the domains of knowledge,we would then use the pluralterm ontology has largely come to mean one of two term ontologies to refer to them collectively.related things. First of all, ontology is a In AI and information-systems literature, however,representation vocabulary, often specialized to some there seems to be inconsistency: sometimes we seedomain or subject matter. More precisely, it is not references to ―ontology of domain‖ and other timesthe vocabulary as such that qualifies as an ontology, to ―ontologies of domain,‖ both referring to the setbut the conceptualizations that the terms in the of conceptualizations for the domain. The former isvocabulary are intended to capture. Thus, translating more consistent with the original (and current) usagethe terms in an ontology from one language to in philosophy.another, for example from English to French, doesnot change the ontology conceptually. In III. Ontology As Content Theoryengineering design, you might discuss the ontology The current interest in ontologies is theof an electronic-devices domain, which might latest version of AI’s alternation of focus betweeninclude vocabulary that describes conceptual content theories and mechanism theories.elements—transistors, operational amplifiers, and Sometimes, the AI community gets excited by somevoltages—and the relations between these mechanism such as rule systems, frame languages,elements—operational amplifiers are a type-of neural nets, fuzzy logic, constraint propagation, orelectronic device, and transistors are a component-of unification. The mechanisms are proposed as theoperational amplifiers. secret of making intelligent machines. At otherIdentifying such vocabulary—and the underlying times,we realize that, however wonderful theconceptualizations—generallyrequires careful mechanism, it cannot do much without a goodanalysis of the kinds of objects and relations that can content theory of the domain on which it is to work.exist in the domain. In its second sense, the term Moreover, we often recognize that once a goodontology is sometimes used to refer to a body of content theory is available, many differentknowledge describing some domain, typically a mechanisms might be used equally well tocommonsense knowledge domain, using a implement effective systems, all using essentiallyrepresentation vocabulary. the same content.For example, CYC1 often refers to its knowledgerepresentation of some area of knowledge as its AI researchers have made several attempts toontology. In other words, the representation characterize the essence of what it means to have avocabulary provides a set of terms with which to content theory. McCarthy and Hayes’theorydescribe the facts in some domain, while the body of (epistemic versus heuristic distinction), 3 Marr’sknowledge using that vocabulary is a collection of three-level theory (information processing, strategyfacts about a domain. level, algorithmsand data structures level, and physical mechanisms level),4 and Newell’s theoryHowever, this distinction is not as clear as it might (Knowledge Level versus Symbol Level)5 allfirst appear. In the electronic-device example, that grapple in their own ways with characterizingtransistor is a component-of operational amplifier or content. Ontologies are quintessentially contentthat the latter is a type-of electronic device is just as theories, because their main contribution is tomuch a fact about its domain as a CYC fact about identify specific classes of objects and relations thatsome aspect of space, time, or numbers. The exist in some domain. Of course, content theoriesdistinction is that the former emphasizes the use of need a representation language. Thus far, predicateontology as a set of terms for representing specific calculuslike formalisms, augmented with type-offacts in an instance of the domain, while the latter relations (that can be used to induce classemphasizes the view of ontology as a general set of hierarchies), have been most often used to describefacts to be shared. There continues to be the ontologies themselves.inconsistencies in the usage of the term ontology. Attimes, theorists use the singular term to refer to a 3.1 Use Of Ontologyspecific set of terms meant to describe the entity and Ontological analysis clarifies the structurerelation-types in some domain. of knowledge. Given a domain, its ontology forms the heart of any system of knowledge representation for that domain. Without ontologies, or the 612 | P a g e
  3. 3. J. Nagaraju, K. Ravi Kumar / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 3, Issue 2, March -April 2013, pp.611-616conceptualizations that underlie knowledge, there vocabulary and syntax to build catalogs thatcannot be a vocabulary for representing knowledge. describe their products. Then the manufacturersThus, the first step in devising an effective could share the catalogs and use them in automatedknowledgerepresentation system, and vocabulary, is design systems. This kind of sharing vastlyto perform an effective ontological analysis of increases the potential for knowledge reuse.the field, or domain. Weak analyses lead toincoherent knowledge bases. An example of whyperforming good analysis is necessary comes fromthe field of databases.6 Consider a domain havingseveral classes of people (for example, students,professors, employees, females, and males).This study first examined the way this databasewould be commonly organized: students,employees, professors, males, and female would berepresented as types-of the class humans. However,some of the problems that exist with this ontology In AI, knowledge in computer systems is thought ofare that students can also be employees at times and as something that is explicitly represented andcan also stop being students. Further analysis operated on by inference processes. However, that isshowed that the terms students and employee do not an overly narrow view. All information systemsdescribe categories of humans, but are roles that traffic in knowledge. Any software that doeshumans can play, while terms such as females and anything useful cannot be written without amales more appropriately represent subcategories of commitment to a model of the relevant world—tohumans. Therefore, clarifying the terminology entities, properties, and relations in that world. Dataenables the ontology to work for coherent and structures and procedures implicitly or explicitlycohesive reasoning purposes. Second, ontologies make commitments to a domain ontology. It isenable knowledge sharing. Suppose we perform an common to ask whether a payroll system―knows‖analysis and arrive at a satisfactory set of about the new tax law, or whether a database systemconceptualizations, and their representative terms, ―knows‖ about employee salaries. Information-for some area of knowledge—for example, the retrieval systems, digital libraries, integration ofelectronic-devices domain. The resulting ontology heterogeneous information sources, and Internetwould likely include domain-specific terms such as search engines need domain ontologies to organizetransistors and diodes; general terms such as information and direct the search processes. Forfunctions, causal processes, and modes; and terms example, a search engine has categories andthat describe behavior such as voltage. subcategories that help organize the search.The ontology captures the intrinsic conceptual The search-engine community commonly refers tostructure of the domain. In order to build a these categories and subcategories as ontologies.knowledge representation language based on the Object-oriented design of software systems similarlyanalysis, we need to associate terms with the depends on an appropriate domain ontology.concepts and relations in the ontology and devise a Objects, their attributes, and their procedures moresyntax for encoding knowledge in terms of the or less mirror aspects of the domain that are relevantconcepts and relations. We can share this knowledge to the application. Object systems representing arepresentation language with others who have useful analysis of a domain can often be reused for asimilar needs for knowledge representation in that different application program.Object systems anddomain, thereby eliminating the need for replicating ontologies emphasize different aspects, but wethe knowledge-analysis process. Shared ontologies anticipate that over time convergence between thesecan thus form the basis for domain-specific technologies will increase. As information systemsknowledge-representation languages. model large knowledge domains, domain ontologies will become as important in general software In contrast to the previous generation of systems as in many areas of AI.knowledge-representation languages (such as KL-One), these languages are content-rich; they have a In AI,while knowledge representation pervades thelarge number of terms that embody a complex entire field, two application areas in particular havecontent theory of the domain. Shared ontologies let depended on a rich body ofus build specific knowledge bases that describe knowledge. One of them is natural-languagespecific situations. For example, different understanding. Ontologies are useful in NLU in twoelectronicdevices manufacturers can use a common ways. First, domain knowledge often plays a crucial role in disambiguation. 613 | P a g e
  4. 4. J. Nagaraju, K. Ravi Kumar / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 3, Issue 2, March -April 2013, pp.611-616 <wos:texbook dc:Creator wos:knuth>A welldesigned domain ontology provides the basis <wos:texbook dc:Title "The TEXbook">for domain knowledge representation. In addition, <wos:knuth foaf:name "Donald Knuth">ontology of a domain helps identify the semantic form an RDF Graph of three statements2. Thecategories that are involved in understanding TEXbook by Knuth is represented by the URIdiscourse in that domain. For this use, the ontology wos:texbook (wos is the namespace prefix of an—plays the role of a concept imaginary—‖Web of Scientists‖ vocabulary, whichdictionary. shall be presented later in this chapter) and described in two statements. The object of the first IV. RDF statement, wos:knuth, is an URI representing the The Resource Description Framework is anextensible infrastructure to express, exchange and 4.1 The Resource Description Frameworkre-use structured metadata [Mil98]: ―Everything isURI‖ Information resources are commonly The meaning of this RDF Graph is ―an informationidentified by Uniform Resource Identifiers (URIs). resource, identified by wos:texbook, has the titleBy generalizing the concept of ―resource‖, whatever "The TEXbook" and was created by somethingis identifiable by an URI can be described in RDF. which is identified by wos:knuth and whose name isIn this way, URIs can be assigned to anything, even "Donald Knuth". Anonymous Resources There arephysical objects, living beings,abstract concepts, etc. situations in which we wish to describe informationIt is important to note that the identifiability does using more complex structures of data than using anot imply retrievability of the resource. The literal string or an URI pointer. For this,principal advantages of this approach are that URIs ―anonymous‖ resources are used: the object ofare a globally unambiguous way to reference a statement can be an anonymous resource—or aresources, and that no centralized authority is blank node—which itself is the subject of othernecessary to provide them. A common way to statements. Such a resource is represented by aabbreviate it is the XML qualified name (or blank node identifier, which is usually denoted as :n,QName) syntax of the form prefix:suffix. For with n being an integer. For example, a moreexample, an URI such as sophisticated version of the above example abouthttp://www.w3.org/TR/rdf-primer/ would be written Knuth’s authorship of the TEXbook would beas w3:rdf-primer/ if it has been agreed that w3 <wos:texbook dc:Creator :1>stands for http://www.w3.org/TR/. RDF Statements <wos:texbook dc:Title "The TEXbook">The atomic structure for RDF specifications is the < :1 foaf:name "Donald Knuth">statement, which is a <subject predicate object>- < :1 rdf:type xy:Person>triple. The information re- 1 For a more thorough < :1 wos:described wos:knuth>introduction see the ―RDF Primer‖ [MM04] or other which states more clearly that the author of thereferences given in subsection A.1.3 38 3. TEXbook is a human, which has a personal namePreliminaries source being described is the subject and is further described in another resourceof the statement and is denoted by an URI. The (wos:knuth).predicate of a statement is an URI referencerepresenting a property, whose property value It is important to note that the blank node identifiersappears as the statement object. The property value carry no meaning; they are used merely for thecan be a resource as well as a literal value. A literal purpose of serialization (e.g., file storage). RDFis a string (e.g., a personal name) of a certain Concepts We can now state more formally thedatatype and may only occur as the object of a triples which are syntactically correct: let ―uris‖ bestatement. RDF triples can be visualized as a the set of URIs, ―blanks‖ the set of blank nodedirected labeled graph, __ __ __subject__ predicate identifiers, and ―lits‖ the set of possible literal/__ ____object__in which subjects and objects are values of whatever datatype (we consider all theserepresented as nodes, and predicates as arcs. sets as infinite). Then (s, p, o) 2 (uris[blanks) ×In [KC04] a drawing convention is given—which (uris) × (uris[blanks [lits) is an RDF statement.will be neglected in this document. According to Observe that there is no restriction to what URIsthis convention nodes representing literals are drawn may appear as statement property. We say that x is aas rectangles and nodes representing URIs as ovals. resource if x 2 uris[blanks, and everythingIn the drawings of this study, however, we need not occurringin an RDF statement is a value (x 2to make these distinctions as we equally treat them uris[blanks [lits). In this document, most of the timeas nodes. For an example of a graph drawn it will be referred to values because the type—URI,following that convention. blank, literal—is not of interest. To recall, a RDF Graph T is a set of RDF statements (T abbreviatesRDF Graph A set of RDF statements is an RDF triples). A subgraph of T is a subset of T. A groundGraph. For example, RDF Graph is an RDF Graph without blank nodes 614 | P a g e
  5. 5. J. Nagaraju, K. Ravi Kumar / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 3, Issue 2, March -April 2013, pp.611-616With univ(T) we denote the set of all values considerable impact: Numerous publications,occurring in all triples of T and call it the universe including tutorials, exist which claim that RDF ―is‖of T; and vocab(T), the vocabulary of T, is the set of a directed labeled graph. The immediate result isall values of the universe that are not blank nodes. the—artificial—distinction between resources andThe size of T is the number of statements it contains properties which many people make.and is denoted by |T|. With subj(T) (respectivelypred(T), obj(T)) we designate all values which occur This prevents users from recognizing the actualas subject simplicity of the RDF model. The results of the(respectively predicate, object) of T. understanding of RDF bounded by the directedLet V be a set of URIs and literal values. We define labeled graph model becomes especially evident inRDFG(V) := { T : T is RDF Graph and vocab(T) _ the limitations of current RDF query languages asV } i.e. the set of all RDF Graphs with a vocabulary studied in [AGH04].included in V .Let M be a map from a set of blanknodes to some set of literals, blanknodes and URI 4.3 Graphs as a Concept of Human Understandingreferences; then any RDF Graph T0 obtained fromthe RDF Graph T by replacing some or all of the Graphs are a successful method to visualize andblank nodes N in T by M(N) is an instance of T. understand complex data. RDF, as a languageConsider an RDF Graph T1, and a bijective map M : developed to annotate and describe informationB1 ! B2 which replaces blank node identifiers of T1 resources and their relations among each other,with other blank node identifiers. Then T2 = M(T1) allows the expression of potentially highlyis an instance of T1, and T1 is an instance of T2 (by interconnected collections of metadata assertions.the inverse of M which is trivially defined). Two For the visualization of RDF data directed labeledsuch RDF Graphs are considered as equivalent. graphs may be employed successfully for not tooEquivalent RDF Graphs are treated as identical RDF complex RDF Graphs. Also, to explain the RDFGraphs, which is in conformance with the notion of model it is natural to use graphs. While theblank nodes as ―anonymous resources‖ Reasons for examples provided, e.g., in the RDF Primer [MM04]Graph Representation of RDF Graphs are are simple and therefore above-mentionedmathematical objects which enjoy wide-spread limitations of directed labeled graphs are not asusage for many tasks, which include the relevant, care should be taken that the (abstract)visualization and analysis of data for humans, graph nature of RDF, the well-defined concept ofmathematical reasoning, and the implementation as RDF Graph and the representation of RDF asa data structure for developing software. These tasks directed labeled graph are not confusedare relevant in the context of RDF data as well, asthis section shall present. V. Conclusion Here by we can conclude that RDF and4.2 Motivation WSDL help us to describe ontologies. Moreover RDF using graphs creates a better understanding of4.2.1 Fixing the Specification ontology to humans and provides good visualization of web servicesThe first specification of RDF in the status of aWWW Consortium Recommendationappeared in References1999 [LS99]. Since then, it has taken five years to [1] Adobe Systems Incorporated. PDFrevise the original specification and to replace it by Reference, Version World Wide Web,a suite of six documents which gained http://partners.adobe.com/asn/tech/pdf/specifirecommendation status just recently, in February cations.jsp, 2003. 22004 [MM04, KC04, Hay04, GB04, Bec04, BG04]. [2] [Ado04] Adobe Developer Technologies.The success of RDF appears to take place at a rather XMP - Extensible Metadata Platform. Worldmodest pace, and one is tempted to conclude that the Widearduously advancing process of specification is one Web,http://www.adobe.com/products/xmp/pdreason for this. The fact that the fs/xmpspec.pdf, 2004. 22004WWWConsortium Recommendation still [3]contains ambiguities as described above gives ttp://www.openrdf.org/doc/users/userguide.motivation to supply a constructive critique and a html, 2004. 10.2.3,proposition for refinement, with the hope to [4] Renzo Angles, Claudio Gutierrez, andcontribute to future revisions of the specification. Jonathan Hayes. RDF Query Languages NeedThe issue—an incomplete definition of a graph Support for Graph Properties. Technicalrepresentation, and a representation with certain Report TR/DCC-2004-3, Universidad delimitations—might appear trivial. However, it has Chile, 615 | P a g e
  6. 6. J. Nagaraju, K. Ravi Kumar / International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 www.ijera.com Vol. 3, Issue 2, March -April 2013, pp.611-616 http://www.dcc.uchile.cl/_cgutierr/ftp/graphp roperties.pdf, 2004. 4.3.1, 4.3.5, 10.3.1, 3[5] Rakesh Agrawal. Alpha: An Extension of Relational Algebra to Express a Class of Recursive Queries. IEEE Trans. Softw. Eng.,14(7):879–885, 1988. 9.3.3[6] Joshua Allen. Making a Semantic Web. World Wide Web, www.netcrucible.com/semantic.html, 2001. 2[7] Boanerges Aleman-Meza, Chris Halaschek, Ismailcem Budak Arpinar, and Amit P. Sheth. Context-Aware Semantic Association Ranking. In I. F. Cruz, V. Kashyap, S. Decker, and R. Eckstein, editors, Proceedings of SWDB’03, 2003.[8] D.B. Lenat and R.V. Guha, Building Large Knowledge-Based Systems: Representation and Inference in the CYC Project, Addison- Wesley, Reading, Mass., 1990.[9] B. Chandrasekaran, ―AI, Knowledge, and the Quest for Smart Systems,‖ IEEE Expert,Vol. 9, No. 6, Dec. 1994, pp. 2–6.[10] J. McCarthy and P.J. Hayes, ―Some Philosophical Problems from the Standpoint of Artificial Intelligence,‖ Machine IntelligenceVol. 4, B. Meltzer and D. Michie, eds., Edinburgh University Press, Edinburgh, 1969, pp.463–502. 616 | P a g e

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