The document discusses the semantic web and ontology inference. It describes how ontologies are used on the semantic web to represent knowledge through concepts and relationships. It then explains different types of ontology inference including TBox inference, ABox inference, and rule-based inference using languages like SWRL. Examples of inference engines that support ontology reasoning are also provided.

1. Inferenceon the Semantic WebMyungjin Lee

2. Artificial Intelligence

3. FussySystemthe intelligence of machinesmethodologyMachineLearningNeuralNetworkgoalmethodologyhasApproachArtificialIntelligenceGeneticAlgorithmmethodologyKnowledgeBaseApproachLogichasApproachbasedonApproaches of AI

4. What is Semantic Web?WebtargetSemanticWebArtificialIntelligencegoalthe intelligence of machinespurposea vision of informationthat is understandable by computers,so that they can performmore of the tedious work involvedin finding, sharing, and combining informationon the web.dc:description

5. approachApproach of Semantic WebSemanticWebKnowledgeBaseApproachLogicSentencebasedonbasedonuserepresentationrepresentationOntologyrepresentationPropositionalLogicPredicateLogicFist OrderLogicDescriptionLogicpartOfpartOf

6. Ontology on the Semantic WebOWLOntologycomponentSCOTcomponentRDFRDFSvocabularycomponentSKOSdc:descriptionXMLcomponentvocabularySIOCURIAn ontology is a formalexplicit specification ofa conceptualization.FOAF

7. Merits of OntologyDatabaseOntologyowl:sameAs¬owl:differentFromimageimagedifferencesdifferencesrdf:Bagrdf:lirdf:lirdf:lia powerofrepresen-tationInferenceSemantics

8. Task of InferenceInferencebeing able to derive new datafrom data that you already knowdc:descriptiontasktaskdc:descriptionRuleInferenceTBoxInferenceOntologyInferencestatementsthat describe a systemin terms of controlledvocabulariestaskdc:descriptiontaskdc:descriptionABoxInferenceTBox-compliant statementsabout that vocabularyto produce valid statementswithin systembased on rule

9. Ontology InferenceOntologyInferenceto derive additional facts to be inferredfrom instance data and class descriptionsbased on own semanticsdc:descriptionRDF SemanticsPerson<x, y> is in IEXT(I(rdfs:subClassOf))if and only if x and y are in ICand ICEXT(x) is a subset of ICEXT(y)ManMyungjin( Man rdfs:subClassOf Person )( Myungjinrdf:type Man )( Myungjinrdf:type Person )

10. TBox InferenceTBoxInferenceOntologyInferencestatementsthat describe a systemin terms of controlledvocabulariesdc:descriptiontask<rdfs:Classrdf:about="http://xmlns.com/foaf/0.1/Document" rdfs:label="Document”> <rdfs:subClassOfrdf:resource="http://xmlns.com/wordnet/1.6/Document"/></rdfs:Class><rdfs:Classrdf:about="http://xmlns.com/foaf/0.1/PersonalProfileDocument”> <rdfs:subClassOfrdf:resource="http://xmlns.com/foaf/0.1/Document"/></rdfs:Class>http://xmlns.com/foaf/0.1/PersonalProfileDocumentrdfs:subClassOf http://xmlns.com/wordnet/1.6/Document

11. ABox InferenceOntologyInferenceABoxInferenceTBox-compliant statementsabout that vocabularydc:descriptiontask<rdf:Propertyrdf:about="http://xmlns.com/foaf/0.1/homepage” rdfs:label="homepage“ > <rdfs:subPropertyOfrdf:resource="http://xmlns.com/foaf/0.1/page"/></rdf:Property><foaf:Personrdf:about="#me" xmlns:foaf="http://xmlns.com/foaf/0.1/"> <foaf:name>Dan Brickley</foaf:name> <foaf:homepagerdf:resource="http://danbri.org/" /></foaf:Person>http://xmlns.com/foaf/0.1 /#me foaf:page http://danbri.org/

12. Rule InferenceRuleInferenceto produce valid statementswithin systembased on ruledc:descriptionif hasParent(?x, ?y)hasParent(?x, ?z) Man(?y) Woman(?z)then hasWife(?y, ?z)hasWifehasParenthasParent

13. SWRL (Semantic Web Rule Language)SWRLHorn-likeRuleMemberSubmissionrepresentationstatuseditorformsubLanguageSWRLTabRuleMLBodyrdf:Seqrdf:lirdf:liHeadplugInProtégéscreenshot

14. Inference Engine for Semantic WebBossama forward chaining rule engine supports SWRLdc:descriptionrdf:typePelletan open-source Java OWL DL reasoner has SWRL-supportdc:descriptionInferenceEnginerdf:typeKAON2an infrastructure for managing OWL-DL, SWRL, and F-Logic ontologiesdc:descriptionrdf:typeRacerPrordf:typeprocessing of rules in a SWRL-based syntax by translating them into nRQL rulesdc:descriptionrdf:typeJenato derive additional RDF assertions, the axioms and rules associated with the reasonerdc:description

15. SMART SystemIntelligenceInformationSystemLabYonseiUniversityjava framework for semantic web applicationlocatedIncreateddc:descriptionSMARTfunctionsupportrdf:Bagrdf:Bagrdf:lirdf:lirdf:lirdf:lirdf:liOntologyProcessSPARQLProcessrdf:liSPARQLRDFRDFSRuleInferenceOntologyInferenceSWRLOWL

16. Example DemoSWRL Ruleif sioc:Post(?x)sioc:Post(?y)sioc:topic(?x, ?a)sioc:topic(?y, ?b)rdf:type(?a, ?z)rdf:type(?b, ?z)then sioc:related_to(?x, ?y)sioc:Postsioc:Postrdf:typerdf:typesioc:related_toclouds-with-siocsample-postsioc:topicsioc:topicsemantic-websws.geonames.orgSPARQL QueryPREFIX sioc: <http://rdfs.org/sioc/ns#>SELECT ?u ?v WHERE{ ?u sioc:related_to ?v .}rdf:typerdf:typesemanticweb

17. Issue of InferenceRDF 상에서 어디에 추론을 쓰지?새로운 관계 발견을 통한 네트워크 분석또 다른 RDF Vocabularies 혹은 도메인 온톨로지와의 관계 규칙 정의 및 추론고민할 문제추론을 위한 표현력과 복잡도많은 룰에 의한 충돌세상사를 반영한 규칙의 생성철저한 준비?표현력의 한계?

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