This slide includes inference technology on the Semantic Web

1. Inferenceon the Semantic Web Myungjin Lee

2. Artificial Intelligence

3. Fussy System the intelligence of machines methodology Machine Learning Neural Network goal methodology hasApproach Artificial Intelligence Genetic Algorithm methodology Knowledge Base Approach Logic hasApproach basedon Approaches of AI

4. What is Semantic Web? Web target Semantic Web Artificial Intelligence goal the intelligence of machines purpose a vision of information that is understandable by computers, so that they can perform more of the tedious work involved in finding, sharing, and combining information on the web. dc:description

5. approach Approach of Semantic Web Semantic Web Knowledge Base Approach Logic Sentence basedon basedon use representation representation Ontology representation Propositional Logic Predicate Logic Fist Order Logic Description Logic partOf partOf

6. Ontology on the Semantic Web OWL Ontology component SCOT component RDF RDFS vocabulary component SKOS dc:description XML component vocabulary SIOC URI An ontology is a formal explicit specification of a conceptualization. FOAF

7. Merits of Ontology Database Ontology owl:sameAs ¬ owl:differentFrom image image differences differences rdf:Bag rdf:li rdf:li rdf:li a power of represen-tation Inference Semantics

8. Task of Inference Inference being able to derive new data from data that you already know dc:description task task dc:description Rule Inference TBox Inference Ontology Inference statements that describe a system in terms of controlled vocabularies task dc:description task dc:description ABox Inference TBox-compliant statements about that vocabulary to produce valid statements within system based on rule

9. Ontology Inference Ontology Inference to derive additional facts to be inferred from instance data and class descriptions based on own semantics dc:description RDF Semantics Person <x, y> is in IEXT(I(rdfs:subClassOf)) if and only if x and y are in IC and ICEXT(x) is a subset of ICEXT(y) Man Myungjin ( Man rdfs:subClassOf Person ) ( Myungjinrdf:type Man ) ( Myungjinrdf:type Person )

10. TBox Inference TBox Inference Ontology Inference statements that describe a system in terms of controlled vocabularies dc:description task <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/PersonalProfileDocument rdfs:subClassOf http://xmlns.com/wordnet/1.6/Document

11. ABox Inference Ontology Inference ABox Inference TBox-compliant statements about that vocabulary dc:description task <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 Inference Rule Inference to produce valid statements within system based on rule dc:description if hasParent(?x, ?y) hasParent(?x, ?z) Man(?y) Woman(?z) then hasWife(?y, ?z) hasWife hasParent hasParent

13. SWRL (Semantic Web Rule Language) SWRL Horn-like Rule Member Submission representation status editor form subLanguage SWRLTab RuleML Body rdf:Seq rdf:li rdf:li Head plugIn Protégé screenshot

14. Inference Engine for Semantic Web Bossam a forward chaining rule engine supports SWRL dc:description rdf:type Pellet an open-source Java OWL DL reasoner has SWRL-support dc:description Inference Engine rdf:type KAON2 an infrastructure for managing OWL-DL, SWRL, and F-Logic ontologies dc:description rdf:type Racer Pro rdf:type processing of rules in a SWRL-based syntax by translating them into nRQL rules dc:description rdf:type Jena to derive additional RDF assertions, the axioms and rules associated with the reasoner dc:description

15. SMART System Intelligence Information System Lab Yonsei University java framework for semantic web application locatedIn created dc:description SMART function support rdf:Bag rdf:Bag rdf:li rdf:li rdf:li rdf:li rdf:li Ontology Process SPARQL Process rdf:li SPARQL RDF RDFS Rule Inference Ontology Inference SWRL OWL

16. Example Demo SWRL Rule if 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:Post sioc:Post rdf:type rdf:type sioc:related_to clouds-with-sioc sample-post sioc:topic sioc:topic semantic-web sws.geonames.org SPARQL Query PREFIX sioc: <http://rdfs.org/sioc/ns#> SELECT ?u ?v WHERE { ?u sioc:related_to ?v . } rdf:type rdf:type semanticweb

17. Issue of Inference RDF 상에서 어디에 추론을 쓰지? 새로운 관계 발견을 통한 네트워크 분석 또 다른 RDF Vocabularies 혹은 도메인 온톨로지와의 관계 규칙 정의 및 추론 고민할 문제 추론을 위한 표현력과 복잡도 많은 룰에 의한 충돌 세상사를 반영한 규칙의 생성 철저한 준비? 표현력의 한계?

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