1. Dept. Computer Science, Korea Univ. Intelligent Information System Lab. 1 Sohn Jong-Soo Intelligent Information System lab. Department of Computer Science Korea University Ontology Languages

2. Dept. Computer Science, Korea Univ. Intelligent Information System Lab. 2 Index 1. Ontology 2. XML 3. RDF 4. OIL 5. DAML 6. OWL

3. Dept. Computer Science, Korea Univ. Intelligent Information System Lab. 3 1. Ontology Definition : Formal, explicit specification of a a shared conceptualization Ontology can be used and shared by agents Ontology languages ■To be understood by humans intuitively ■Capturing of meaning (semantics) of data ■Inference mechanism with completeness, preciseness and efficiency ■Interoperability and compatibility  Combined with web languages s.a. XML and RDF

4. Dept. Computer Science, Korea Univ. Intelligent Information System Lab. 4 1. Ontology Crucial role in enabling web-based knowledge processing, sharing and reuse ■Human-beings and machines communicate each other  common understanding of topics between people and applications

5. Dept. Computer Science, Korea Univ. Intelligent Information System Lab. 5 1. Ontology Conceptual structures for machine processible data on the web ■Formal tools to structure semantic data ■Formal conceptualizations of particular domains Metadata schema with controlled vocabulary of concepts ■Semantic metadata for web pages ■RDF & RDFS as metadata formats

6. Dept. Computer Science, Korea Univ. Intelligent Information System Lab. 6 2. XML (eXtensible Markup Language) Standard markup language to represent the user- defined markup language meta markup language ■Markup language to define another markup language Simple, but flexible text-format defined from SGML Large-scale electronic publishing to meet the role in the exchange of wide variety of data on the web and elsewhere Hierarchical structure with tag (DTD) DTD Document Structure (Markup Language) XML Document Contents (instance) Style sheet Style language

7. Dept. Computer Science, Korea Univ. Intelligent Information System Lab. 7 2. XML (eXtensible Markup Language) XML related standards ■DTD (Document Type Definition)  Defines the logic structure of XML documents  Defines contents & attributes of each component  Defines objects ■XSL (eXtensible Style Sheet)  Defines the style to each component of XML documents  Documents transformation ■CSS (Cascading Style Sheet)  Some functionality as XSL  Limitation in the style definition

8. Dept. Computer Science, Korea Univ. Intelligent Information System Lab. 8 2. XML (eXtensible Markup Language) Advantages ■Data representation ■ structured & independent ■Data sharing and interoperability ■Hierarchical, composite data Disadvantages ■Lack of representation of relationship between objects ■Lack of representation of data meaning ■Lack of inheritance of meaning

9. Dept. Computer Science, Korea Univ. Intelligent Information System Lab. 9 3. RDF (Resource Description Framework) Markup language based on XML syntax Developed to representation the multiple, various resources dispersed in the distributed web environment Used as a basis for the other markup language Data representation : triple representation as follow

10. Dept. Computer Science, Korea Univ. Intelligent Information System Lab. 10 3. RDF (Resource Description Framework) Advantages ■Representation of data with the meaning ■Environment in which computer can understand and process the data ■Flexible capability to representation the meta data ■Mean of information exchange in heterogeneous distributed environment ■Description of constants by the semantic network Disadvantages ■Lack of affection inference mechanism ■Weak in the representation of semantic of data

11. Dept. Computer Science, Korea Univ. Intelligent Information System Lab. 11 3. RDF (Resource Description Framework) Sohn JongSoo mis026@korea.ac.kr

12. Dept. Computer Science, Korea Univ. Intelligent Information System Lab. 12 4. OIL (Ontology Inference Layer) Satisfies the requirement of semantic web Hierarchical layer structure for extension

13. Dept. Computer Science, Korea Univ. Intelligent Information System Lab. 13 4. OIL (Ontology Inference Layer) Based on Frame-based System, Description Logic and Web Languages OIL Frame-based system: Epistemological Modeling Primitives Description Logics: Formal Semantics& Reasoning Support Web language: XML and RDF-based syntax

14. Dept. Computer Science, Korea Univ. Intelligent Information System Lab. 14 4. OIL (Ontology Inference Layer) Advantages ■Hierarchical extensions ■Effective inference mechanism based on the Description Logic ■Well-defined semantics Disadvantages ■Impossible to define the default-value ■Impossible to provide the meta-class ■Impossible to support the concrete domain  Limitation in the OIL extension and ontology transformation

15. Dept. Computer Science, Korea Univ. Intelligent Information System Lab. 15 5. DAML (DARPA Agent Markup Language) Based on XML and RDF Combines the advantage of various, multiple semantic web languages ■Combination of DAML + OIL ■DAML-S  Automatic Web Service retrieval and execution ■DAML-L  Logic representation

16. Dept. Computer Science, Korea Univ. Intelligent Information System Lab. 16 5. DAML (DARPA Agent Markup Language) Advantages ■Powerful in the representation of meaning and constraints ■Support for the XML-Schema data type ■Support well-defined semantics ■Support default value Disadvantages ■Can’t exclude the RDF and XML ■Can’t be formal language ■Less extensible compared with OIL

17. Dept. Computer Science, Korea Univ. Intelligent Information System Lab. 17 5. DAML (DARPA Agent Markup Language) <rdf:RDF xmlns:rdf=”http://www.w3.org/1999/02/22/-rdf-syntzs-ns#” xmlns:rdfs=”http://www.w3.org/TR/1999/PR-rdf-schema-19990303#” xmlns:daml=”http://www.daml.org/2001/03/daml+oil#” xmlns = “http://www.daml.org/2001/03/daml+oil#”> 1.0 Animal

18. Dept. Computer Science, Korea Univ. Intelligent Information System Lab. 18 6. OWL (Web Ontology Language) Three species of OWL ■OWL full is union of OWL syntax and RDF ■OWL DL restricted to FOL fragment ( ¼ DAML+OIL) ■OWL Lite is “ easier to implement ” subset of OWL DL Semantic layering ■DL semantics officially definitive OWL DL based on SHIQ Description Logic ■In fact it is equivalent to SHOIN (D n ) DL OWL DL Benefits from many years of DL research ■Well defined semantics ■Formal properties well understood (complexity, decidability) ■Known reasoning algorithms ■Implemented systems (highly optimised)

19. Dept. Computer Science, Korea Univ. Intelligent Information System Lab. 19 6. OWL (Web Ontology Language) Relationships between classes ■equivalentClass ■subClassOf ■Intersection, union, complement, disjunction Relationships between instances ■sameAs, differentFrom Properties of properties ■Domain, Range ■Cardinality ■Transitive, Symmetric ■allValuesFrom, someValuesFrom ■Functional, InverseFunctional Relationships between properties ■subPropertyOf ■inverseOf

20. Dept. Computer Science, Korea Univ. Intelligent Information System Lab. 20 6. OWL (Web Ontology Language) RDFS syntax

21. Dept. Computer Science, Korea Univ. Intelligent Information System Lab. 21 7. Conclusion 온톨로지를 표현하는 언어 ■ 많은 온톨로지 언어 중 중요하게 연구된 것을 위주로 조사 ■ 소개된 것 외에도 Ontolingua, SHOE, TopicMap 등이 있음 W3C 의 표준화 추세 ■W3C 에서 표준으로 제정한 OWL 이 가장 유력해 보임 ■OWL 에 대한 연구가 가장 활발 ■OWL 을 확장하여 표현력을 높이는 노력이 보임 My impression ■ 비교적 예전의 언어를 이용하여 example 을 만들기가 쉽지 않 았음 ■ 시맨틱 웹 및 지능형 웹 서비스의 과거 및 현재를 돌아봄으로 써 발전 방향에 대하여 다시한번 생각할 수 있는 계기 마련

22. Dept. Computer Science, Korea Univ. Intelligent Information System Lab. 22 Thank you