1. Introduction Subhashis Das Ontology languageSonali Kishore Kalani Ontology Engineering Tools Amit Kumar Shaw Application of Ontologies Mayukh Biswas Conclusion Anurodh Kumar Sinha

2. Markup consists of: rendering information (e.g., font size and colour) Hyper-links to related contentSemantic content is accessibleto humans but not (easily) tocomputers 3. WWW2002 The elevenvh inveqnavional soqld side seb confeqence Sheqavon saikiki hovel Honoltlt, hasaii, USA 7-11 may 2002 1 locavion 5 dayu leaqn inveqacv Regiuveqed paqvicipanvu coming fqom atuvqalia, canada, chiledenmaqk, fqance, geqmany, ghana, hongkong, india, iqeland, ivaly, japan, malva, nes zealand, vhenevheqlandu, noqsay, uingapoqe, usivzeqland, vhe tnived kingdom, vhe tniveduvaveu, vievnam, zaiqe Regiuveq vhnos On vhe 7May Honoltlt sill pqovide vhebackdqop of vhe elevenvh inveqnavionalsoqld side seb confeqence. Thiupqeuvigiotu evenv Speakequ confiqmed Tim beqnequ-lee Tim iu vhe sell knosn invenvoq of vheWeb, Ian Fouveq Ian iu vhe pioneeq of vhe Gqid, vhenev geneqavion inveqnev 4. External agreement on meaning of annotations E.g., Dublin Core Agree on the meaning of a set of annotation tags Problems with this approach Inflexible Limited number of things can be expressed Use Ontologies to specify meaning of annotations Ontologies provide a vocabulary of terms New terms can be formed by combining existing ones Meaning (semantics) of such terms is formally specified Can also specify relationships between terms in multiple ontologies 5. OntologyRelationships, constraint s, rulesStrong SemanticsThesaurus Equivalence, homographic, hierarchical and associative relationships TaxonomyStructure, hierarchy, parent- child relationshipsControlled vocabularyWeak Semantics 6. -----By Thomas Robert (Tom) Gruber (1994)A formal, explicit specification of a shared conceptualizationmust be machine not private to some individual,understandablebut accepted by a group types of concepts andan abstract model of some constraints must be clearlyphenomenon in the world formed bydefined identifying the relevant concepts ofthat phenomenon 7. v To share common understanding of the structure of information among people orsoftware agents.v To enable reuse of domain knowledge.v To make domain assumptions explicit.v To separate domain knowledge from the operational knowledge.v To analyze domain knowledge. 8. r Defining terms in the domain and relations among themv Identifying the domain.v Defining concepts in the domain (classes). (human, animal, food, table, movies, etc..)v Arranging the concepts in a hierarchy (superclass -Subclass hierarchy). (Ex-Animal-herbivorous -omnivorous-carnivorous)v Defining which attributes and properties classes can have and constraints on theirvalues.Attributes (data properties), i.e. human has properties ofgender, height, weight, father, mother, etc.Properties (Relations), i.e. Indian Statistical Institute is located in Bangalore. HERBIVORES= only eat vegetables for example elephants are herbivoresCARNIVORES= only eat meat for example tigers are carnivoresOMNIVORES= omnivores eat both meat and plants for example dogs are omnivoresv Defining individuals and filling in properties values. 9. The three major uses of Ontologies are:v To assist in communication between humans and computer.v To achieve interoperability and communication among software systems.v To improve the design and quality design and the quality of software system. 10. The term procedure used by one toolis translated into the term method used by the other via the ontology,whose term for the same underlyingprocedure concept is process. give me the procedure forviewer here is the give me theprocedure for translatorprocedure = ???process forprocedure =process Ontology??? = processgive me the METHOD =translator METHOD process for here is here is thethe process for METHOD formethodlibrary 11. Chair 12. A piece of furniture consisting of aseat, legs, back, and oftenarms, designed to accommodate oneperson.Chair 13. Chair Seat Stool Bench 14. Something I can sit ?????Chair Seat Stool Bench 15. Something I can sit SittableChair SeatStool Bench 16. Something I can sit on SittableTableChair Seat Stool Bench 17. Sittable For_sittingTableChair Seat Stool Bench 18. material is_aroomSittableis_aWoodclassroomis_a is_aFor_sitting TableDining room is_a is_ais_a is_aChair SeatStoolBench 19. material made_ofis_aroomSittable is_aWoodmade_ofclassroomis_a is_aFor_sittingTableDining room is_a is_ais_a is_aChair SeatStool Bench 20. Ontologies generally describe:v Classessets, collections, or types of objects (Ex-Person, animal, food, table, etc.)v Individuals the basic or ground level objects (Ex- Subhashis Das is an Individual of ClassPerson)v Relationshipsways that objects can be related to one another (Subhashis Das lives in Kolkata )v Attributesproperties, features, characteristics, or parameters that objects can have andshare ( Subhashis Das has properties of gender, height, weight, hair colour, mobile no, etc) 21. From a practical view, ontology is the representation of something we know about.Ontologies" consist of a representation of things, that are detectable or directlyobservable, and the relationships between those things. 22. Sir Ratan Naval Tata (born 28 December 1937) is an Indian businessman whobecame chairman (1991 ) of the Tata Group, a Mumbai-based conglomerate. Heis a member of a prominent family of Indian industrialists and philanthropists (Tatafamily). Tata received the Padma Bhushan, one of Indias most distinguishedcivilian awards, in 2000 and Padma Vibhushan in 2008. He has also been ranked asIndias most powerful CEO. Ratan Tata was adopted to famous Tata , a prominentfamily belonging to the Parsi community. Ratan is the grandson of Tata groupfounder Jamsedji Tata. (http://en.wikipedia.org/wiki/Ratan_Tata)Relations: is-a, received, is-CEO-of, is_granson_of,Ratan Tata has Properties ofGender: maleDOB: 28 Dec, 1937Race: ParsiAdministrative role: CEO of Tata group 23. -Sonali Kalani 24. Requirements for an ontology language A well defined syntax A well-defined semantics Efficient reasoning support Adequate expressive power Convenience of expression 25. RDF/RDF Schema Used for describing resources on web Written in XML W3C recommendation RDF Schema is an extension of RDF Provides the framework to describe application-specific classes and properties instead of actualapplication classes and properties Similar to classes in OOP languages 26. Basic Building Blocks of RDF Schema Classes and their instances Binary properties between classes Organization of classes and properties inhierarchies Domain and range restrictions 27. Limitations of RDF Schema Local Scope of properties Disjointness of classes Boolean combinations of classes Cardinality restrictions Special characteristics of properties 28. OWL, a Web Ontology Language OWL stands for Web Ontology Language OWL is for processing information on the web Three sublanguages OWL Full OWL DL OWL Lite Build on top of XML RDFS Similar to RDF but with much stronger syntax and largervocabulary OWL is a W3C standard 29. OWL Full Maximum expressiveness Fully upward compatible with RDF OWL Full allows an ontology to enhance themeaning of the pre-defined (RDF or OWL)vocabulary All language constructors can be used in anycombination as long as it is legal RDF Reasoning software are not able to support everyfeature of OWL Full 30. OWL DL Based on Description Logic Maximum expressiveness without losingcompleteness Widely available reasoning systems Constraints: Vocabulary partitioning Explicit typing Property separation No transitive cardinality restrictions Restricted anonymous classes 31. OWL Lite Must be an OWL DL ontology The constructorsowl:oneOf, owl:disjointWith, owl:unionOf, owl:complementOF and owl:hasValueare not allowed Cardinality statements can be made only on values 0or 1. owl:equivalentClass cannot be made betweenanonymous classes, but only between classidentifiers 32. RDFOWLOWLOWLXMLRDF Schema LiteDLFull Increasing Semantic Expressiveness 33. Building Blocks in OWL[contd.] Ontology declaration (XML syntax) Ontology metadata (information about the ontology)An example OWL ontologyUniversity Ontology 34. Building Blocks in OWL Classes Every class is a descendant of owl:Thing Classes are defined using owl:Class Equivalence is defined using owl:equivalentClass Subsumption Provided by owl:subClassOf Partitions Disjoint partition owl:disjointWith Exhaustive partition owl:oneOf 35. Building Blocks in OWL[contd.] Attributes (properties) Datatype properties: Allows to describe a specificaspect of a concept Based on XSD data types The range specifies the data type The domain specifies the class to which the property is referred E.g.: Phone, title, age Object properties: Attributes that definerelationships between classes (Relations) E.g.: isTaughtBy(Class(course), Class(professor)) 36. Building Blocks in OWL[contd.] Relationships Directed From one concept to another, no vice versa Defined through object properties Domain: the class(es) from which the relation departs Range: the relation destination(s) Subsumption between relationships is possible 37. Building Blocks in OWL[contd.] Instances (Individuals) No unique name assumption in OWL If two instances have a different name or ID this doesnot imply that they are different individuals E.g.: Queen Elizabeth, The Queen and Elizabeth Windsor might all refer to the same individual It must be explicitly stated that individuals are thesame as each other, or different to each other Defined by means of rdf:Description + rdf:Type 38. Building Blocks in OWL[contd.] Advanced constructs OWL supports several advanced constructs to defineclasses and relationships Constraints defined on attribute values (either objector datatype properties) 39. Special Properties owl:TransitiveProperty owl:SymmetricProperty owl:FunctionalProperty owl:InverseFunctionalProperty 40. OWL Class ConstructorsConstructorDL Syntax ExampleModal SyntaxintersectionOfC1 Cn Human Male C1 CnunionOf C1 CnDoctor Lawyer C1 CncomplementOfC MaleConeOf{x1} {xn} {John} {Mary} x1 xnallValuesFrom P.C hasChild.Doctor [P]CsomeValuesFromP.C hasChild.Lawyer

CmaxCardinalitynP1hasChild[P]n+1minCardinalitynP2hasChild

n 41. OWL AxiomsAxiomDL SyntaxExamplesubClassOfC1 C2 Human Animal BipedequivalentClassC1 C2Man Human MaledisjointWith C1 C2Male FemaleSameIndividualAs {x1} {x2}{President Bush} {G W Bush}DifferentFrom {x1} {x2} {John} {Peter}subPropertyOfP1 P2 hasDaughter hasChildequivalentProperty P1 P2 Cost PriceinverseOf P1 P2hasChild hasParenttransitiveProperty P+ Pancestor+ ancestorfunctionalProperty T 1PT 1hasMotherinverseFunctionalPropertyT 1PT 1hasSSN 42. - Amit Kumar Shaw 43. The are many Ontology tools are available in the presenttimes such asProtg, OntoEdit, Ontolingua, OilEd, pOWL etc. Protg is a free, open-source platform to construct domainmodels and knowledge-based applications with ontologies. It provide Graphical User Interface for development of RDFand OWL statement. 44. Go http://protege.stanford.edu/download/registered.htmltodownload Protg Protg OWL editor is built with the full installation ofProtg platform. During the install process, choose theBasic+OWL option. For more details:http://protege.stanford.edu/doc/owl/getting-started.html 45. Protg There are two main ways of modeling ontologies: Frame-based OWL Each has its own user interface Protg Frames editor: enables users to build and populate ontologies that are frame-based, in accordance with OKBC (Open Knowledge Base Connectivity Protocol). Protg OWL editor: enables users to build ontology for the Semantic Web, in particular to OWL Classes Properties Instances Reasoning 46. Building an OWL OntologyCreate a new OWL project Start protg A new empty Protg-OWL project has been created. Save it in your local file as pizza.owl 47. Named Classes Go to OWL Classes tab The empty class tree contains one class called owl:Thing, which is superclass ofeverything. Create subclasses Pizza, PizzaTopping and PizzaBase. They are subclasses ofowl:Thing. 48. Disjoint classesHow to say that Pizza, PizzaTopping and PizzaBase classes are disjoint. 49. OWL Properties OWL Properties represent relationships betweentwo objects. There are two main properties: Object properties: link object to object datatype properties: link object to XML Schemadatatype or rdf:literal OWL has another property Annotationproperties, to be used to add annotationinformation to classes, individuals, OntoGraf etc. 50. Inverse Properties Each object property may have a correspondinginverse property. If some property links individual a to individualb, then its inverse property will link individual bto individual a. 51. Functional Properties If a property is functional, for a given individual, there canonly be at most one individual to be related via thisproperty. For a given domain, range must be unique Functional properties are also known as single valuedproperties. 52. Inverse Functional Properties If a property is inverse functional, then its inverseproperty is functional. For a given range, domain must be unique. 53. Functional v/s Inverse Functional Properties FunctionalProperty vs InverseFunctionalProperty domain range example Functional For a givenRange is hasFather: A hasFatherProperty domain uniqueB, A hasFather C B=CInverseFunctional Domain is For a given hasID: A hasID B, C Propertyunique range hasID B A=C 54. Transitive Properties If a property is transitive, and the property related individuala to individual b, and also individual b to individual c, thenwe can infer that individual a is related to individual c viaproperty P. 55. Symmetric Properties If a property P is symmetric, and the property relatesindividual a to individual b, then individual b is also relatedto individual a via property P. 56. Property: domains and ranges Properties link individuals from the domain to individualsfrom the range Let us see the live demo in Protg Software 57. Ontology ApplicationThe topic can be discussed using two approaches: Discussing the Ontology application domains Discussing the Ontology integration in Applications (i.e. Context-aware Applicationsusing Ontology) 58. Ontology Application Domains/ Key Areas Information retrieval procedure Knowledge representation/sharing Semantic Digital Libraries Software engineering Natural-Language processing Multi-agent systems 59. Information retrieval procedure Agricultural Ontology Service (AOS) The AOS/CS will serve as a multilingual repository of concepts in the agricultural domain providing ontological relationships and a rich, semantically sound terminology. the purpose of the AOS is to achieve: better indexing of resources, better retrieval of resources, and increased interaction within the agricultural community. 60. Information retrieval procedureThe Agricultural Ontology Service (AOS) (A Tool for Facilitating Access to Knowledge)Food and Agriculture Organization of the United Nations (FAO)Library and Documentation Systems Division, AGRIS/CARIS and Documentation GroupRome, Italy, June 2001, Draft 5a, September 2001 61. Agricultural Ontology Service Concept Server (AOS/CS)Initially developed using relational databaseNow new model is developed using Web Ontology Language (OWL)The new developed model in OWL will serve as a skeleton for building agriculture domain ontologies.*Lauser , B., Sini, M., Liang, A., Keizer, J. and Katz, S., From AGROVOC to the Agricultural Ontology Service / Concept Server. An OWLmodel for creating ontologies in the agricultural domain, Networked Knowledge Organization Systems and Services, The 5th EuropeanNetworked Knowledge Organization Systems (NKOS) Workshop, Workshop at the 10th ECDL Conference, Alicante, Spain, September21, 2006. 62. Agricultural Ontology Service Concept Server (AOS/CS) The multilingual issue (lexicalization) is handled using three levelsof representations i.e. Concepts (the abstract meaning), Term ( language-specific lexical form) and Term variant ( the range of forms that can occur for each term) On the Bases on the above representation inter-level relations aredefined i.e. Concept to Term (has_lexicalization) Term to String (has_acronym, has_spelling_variant, has_abbreviation) Concept to Concept (is_a) Term to Term (is_synonym_of, is_translation_of) 63. Agricultural Ontology Service Concept Server (AOS/CS)The Basic ModelURI Disambiguation The Concept-to-Conceptinterface 64. Agricultural Ontology Service ConceptServer (AOS/CS)Term-to-Term InterfaceTerm-to-String Interface Classification Schemes e.g. University of Bekkeley has thefollowing variants Model has the support of two l UCB, Cal, UC Berkeley, University of clasification schemes namely Calfornia at BerkeleyAGRIS/CARIS and FAO priority areas l These relationships are modeled asl c_classification_scheme properties of the data type l r_belongs_to_scheme r_has_term_variant l r_has_category l r_has_sub_category 65. Semantic Digital Libraries* To provide uniform access to Digital Libraries to deal with structural and semanticheterogeneitiesThree application areas of ontologies (referred JeromeDL and BRICKS semantic digitallibrary projects) Bibliographic Ontologies Ontologies for Content Structures Community-aware Ontologies*Kruk, R.S., Haslhofer, B., Piotrowski, Westerski, A. and Woroniecki, T. The Role of Ontologies in Semantic Digital Libraries, NetworkedKnowledge Organization Systems and Services, The 5th European Networked Knowledge Organization Systems (NKOS)Workshop, Workshop at the 10th ECDL Conference, Alicante, Spain, September 21, 2006. 66. Semantic Digital Libraries Ontologies for Content Structures By including structural concepts in ontologies, electroniccontents can be retrieved. Community-Aware Ontologies In semantic digital libraries, besides storing contents and metadata, track of users, their interactions, and their knowledgecan be incorporated into the systems using community-awareontologies 67. Conclusion Anurodh Kumar Sinha 68. Recent Developments Semantic Search Ontology Based Information Retrieval1)Mental Model2)User-Question Model3)System Resource Model4)System Query Model 69. Semantic Digital libraries1.Ontologies can be used to: (i) organize bibliographic descriptions,(ii) represent and expose document contents, (iii) share knowledge amongst users 70. Semantic Social Network Social Network + Semantic Web 1)Social Layer 2)Ontology Layer 3) Concept Layer 71. Use of Ontology in Linked Data The IRW ontology can be used as a tool to make Linked Data more self-describing and to allow inference to be used to test for membership in variousclasses of resources The IRW ontology this in turn allows the semantic validation, to be able todescribe and infer in detail the types of resources that can be interacted withvia HTTP, which is useful for both tools like EARL that record validation of Webstandards to be implemented in a reliable fashion, which is useful for error-reporting on the Web in general and HTTP in particular IRW clarifies the interactions between the hypertext Web and LinkedData, allowing Linked Data spiders to keep track of important provenanceregarding the identity of resources, and to characterise the resources correctlyfor semantic validation and error detection. 72. Notion of consistency: The notion of consistency which is appropriate in this networkof ontologies in order to meet the requirements of future real-life application needs tobe analyze. Evolution of ontologies and metadata: One has to investigate which kind of metadataare suitable for supporting the evolution of these network ontology. Reasoning: A basic open issue is the development of reasoning mechanisms in thepresence of inconsistencies between these networked ontology. 73. Semi-automatic methods: Major obstacle to developing ontology-based application incommercial setting. Therefore, the tight coupling of manual methods with automaticmethods is needed. Design patterns: Analogous to the development of design patterns in softwareengineering of ontologies has to be improved by the development of pattern librariesthat provide ontology engineers with well engineered and application proven ontologypatterns that might be used on building block. Economic aspects: In commercial settings, one needs well-grounded estimations forthe effort one has to invest for building up the required ontologies in order to be ableto analyses and justify that investment. Up to now, only very preliminary methods existto cope with these economic aspect 74. Conclusion Ontologies enable a sound reasoning framework for making machines to becontextual, discernable and relevant tool to produce semantic informationretrieval results Helps to reason and turn on the meaning in searching, i.e, thus add morerelevance in searching information 75. References1. Amandeep S. Sidhu, Tharam S. Dillon,Fellow IEEE, Elizabeth Chang,MemberIEEE, Creating a Protein Ontology Resource2.David Vallet, Miriam Fernndez, and Pablo Castells, A n Ontology-BasedInformation Retrieval Model3. Francois Bry, Tim Furche, Paula-Lavinia Patranjan, and Sebastian Schaffert,Data Retrieval and Evolution on the (Semantic) Web: A Deductive ApproachProtege Ontology Libraries http://protegewiki.stanford.edu/index.php/Protege_Ontology_LibraryProtege tutorial http://www.co-ode.org/resources/tutorials/Protege Website http://protege.stanford.edu/doc/users.html http://protege.stanford.edu/ 76. 4.Guoqian Jiang, Katsuhiko Ogasawara, Naoki Nishimoto, Akira Endoh, TsunetaroSakurai, FCAView Tab: A Concept-oriented View Generation Tool forClinical Data Using Formal Concept Analysis5.G. Marcos, H. Eskudero, C. Lamsfus , M.T. Linaza, Data Retrieval From aCultural Knowledge Database6. Jacob Khler and Steffen Schulze-Kremer, The Semantic Metadatabase(SEMEDA): Ontology based integration of federated molecular biologicaldata sources7. Jeff Heflin and James Hendler, Searching the Web with SHOE