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13.12.2005 1 Intelligent Systems Lecture 24 Ontologies. Semantic WEB (based on presentation of Forschungszentrum Informatik at the University of Karlsruhe,

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Presentation on theme: "13.12.2005 1 Intelligent Systems Lecture 24 Ontologies. Semantic WEB (based on presentation of Forschungszentrum Informatik at the University of Karlsruhe,"— Presentation transcript:

1 13.12.2005 1 Intelligent Systems Lecture 24 Ontologies. Semantic WEB (based on presentation of Forschungszentrum Informatik at the University of Karlsruhe, Germany)

2 13.12.2005 2 Definitions An ontology is a specification of a conceptualization An ontology is a description (like a formal specification of a program) of the concepts and relationships that can exist for an agent or a community of agents. Purpose of enabling knowledge sharing and reuse. In that context, an ontology is a specification used for making ontological commitments.

3 13.12.2005 3 Definitions (2) The subject of ontology is the study of the categories of things that exist or may exist in some domain. The product of such a study, called an ontology, is a catalog of the types of things that are assumed to exist in a domain of interest D from the perspective of a person who uses a language L for the purpose of talking about D. The types in the ontology represent the predicates, word senses, or concept and relation types of the language L when used to discuss topics in the domain D. An uninterpreted logic, such as predicate calculus, conceptual graphs, or KIF, is ontologically neutral. It imposes no constraints on the subject matter or the way the subject may be characterized. By itself, logic says nothing about anything, but the combination of logic with an ontology provides a language that can express relationships about the entities in the domain of interest.

4 13.12.2005 4 Definitions (2) An informal ontology may be specified by a catalog of types that are either undefined or defined only by statements in a natural language. A formal ontology is specified by a collection of names for concept and relation types organized in a partial ordering by the type- subtype relation. Formal ontologies are further distinguished by the way the subtypes are distinguished from their supertypes: an axiomatized ontology distinguishes subtypes by axioms and definitions stated in a formal language, such as logic or some computer- oriented notation that can be translated to logic; a prototype-based ontology distinguishes subtypes by a comparison with a typical member or prototype for each subtype. Large ontologies often use a mixture of definitional methods: formal axioms and definitions are used for the terms in mathematics, physics, and engineering; and prototypes are used for plants, animals, and common household itemsaxiomatized ontology prototype-based ontology

5 13.12.2005 5 Agenda 13.12.2005 Introduction & Motivation Introduction - Semantic Web Semantic Web Applications Semantic Web Technology Next Steps

6 13.12.2005 6 Knowledge Management Ontologies and Metadata Machine Learning Web Technologies and Standards E-Learning Data, Text Web Mining Information Extraction Web Portals Search engines Metadata-driven Applications Basic Technologies Application Fields Application fields and technologies

7 13.12.2005 7 Motivation WWW is a success, measured in –the number of users –the number of available documents Goal-driven access to information is problematic, because Web content has to be interpreted, combined and processed by humans. We are currently on the way to a next generation Web, building on the existing WWW - the Semantic Web which will make contents also for machines accessible and interpretable !

8 13.12.2005 8 ArpanetInternet/WWWSemantic Web 19651985200019751995 Packets Objects Concepts 2005... On the Way to a Global Information Structure

9 13.12.2005 9 Agenda 13.12.2005 Introduction & Motivation Introduction - Semantic Web Semantic Web Applications Semantic Web Technology Next Steps

10 13.12.2005 10 “Information Management: A Proposal“, Tim Berners- Lee, CERN, 1989 The Origin of the WWW

11 13.12.2005 11 Semantic Web – Bringing the Web to its Full Potential HTML mit Hyperlinks Relational Metadata URI-SHA URI-STEFAND URI-DAMLPROJ WORKS-IN COOPERATES- WITH WORKS-IN „ Darpa Agent Markup Language“ PROJECT RESEARCHER PERSON subClassOf range domain Ontology TOP COOPERATES WITH WORKS-IN NAME domain range NAME subClassOf SYMMETRIC

12 13.12.2005 12 Ontologies In its classical sense ontology is a philosophical discipline. In Computer Science: Formal specification of a domain of interest in the form of a concept system Targets: –Shared understanding of a domain of interest –Formal description of the meaning of terms and relations –Machine executable (e.g. query for all relations of the concept „HOTEL“)

13 13.12.2005 13 Metadata are „data about data“, e.g. –Library classification systems –The Yahoo! Categorization –Microsoft Office Document Properties Metadata in the Semantic Web is complex structured (based on predefined ontologies): Relational Metadata http://www.w3c.org/http://www.w3c.org http://www.w3.org/Home/Lassilahttp://www.w3.org/Lassila Ora Lassila s:Name s.Organizations. s:Person rdf:type rdf:type s:email lassila@w3.org s:worksAt s:email s:Name

14 13.12.2005 14 Agenda 13.12.2005 Introduction & Motivation Introduction - Semantic Web Semantic Web Applications –Skills & Human Resources –Semantic Intranet Portals –Interoperability in Tourism –Web Services –Virtual Museum Semantic Web Technology Next Steps

15 13.12.2005 15 Ontologies/Metadata in Human Resources Usage of skill ontologies: – Automatic extraction of skills (from applications) – Semantic Ranking – Competency Analysis via Data Mining – Relation to E-Learning with skills

16 13.12.2005 16 2. Digitalization & Text Generation via OCR 3. Automatic Skill Extraction using Shallow Parsing 3. Intranet – Employee Marketplace 1.Paper-based Application Ontologies/Metadata in Human Resources

17 13.12.2005 17 Automatic Generation of Metadata Via OCR from written documents extracted Predefined skill ontology with metadata and lexicon

18 13.12.2005 18 Semantic-Driven Intranet Portal (I) Requirements: Develop domain-specific terminology for topics Automatically generate Yahoo-like structure for this terminology Allow to add further, complex structured information to the terminology Techniques: Ontology Engineering Discovering of Web Documents via Focused Crawling Automatic Classification of Documents into Ontology Cooperative Metadata Engineering

19 13.12.2005 19 Semantic-Driven Intranet Portal (II) Human Resource Strategy: - Define relevant topics in the form of an ontology - Cooperatively add further information in the form of metadata!  Semantic Portals for HR strategy - Search relevant Web resources

20 13.12.2005 20 Virtual Museum (I)

21 13.12.2005 21 Virtual Museum (II)

22 13.12.2005 22 News Services - Content Syndication with RSS (I) 13.12.2005 NEWS ARE FREE!

23 13.12.2005 23 News Services - RDF Site Summary RSS (II)

24 13.12.2005 24 Content Services - OntoWeb Community Portal OntoWeb Community Annotated Web Pages Generated Content Objects Participating Site 2 { } Participating Site n { } Participating Site 1 { }... Ontology Browse & Query Front End Content Syndication Service http://www.ontoweb.org

25 13.12.2005 25 General Web Services 13.12.2005 Web services –perform functions, which can be anything from simple requests to complicated business processes! –will transform the Web from a collection of information to a distributed device of computation Web services clearly require a semantic-driven description! => Semantic Web Enabled Web Services

26 13.12.2005 26 HARMONISE – Interoperability in Tourism The tourism industry is essentially an information business where data interoperability is necessary to create dynamic markets and cooperation. Build bridges between different tourism marketplaces via Semantic Web technologies  MAPPING DISCOVERY! An ontology will mediate between the different underlying representations.

27 13.12.2005 27 Agenda 13.12.2005 Introduction & Motivation Introduction - Semantic Web Semantic Web Applications Semantic Web Technology –A Layered Approach –RDF(S) –KAON Open Source Infrastructure Next Steps

28 13.12.2005 28 The Semantic Web As By its Inventor

29 13.12.2005 29 XML and its relation to the Semantic Web „XML only provides an alphabet, not a vocabulary“. [Forrester Report, December 2001] The languages french and english use the same alphabet. => Can all french people communicate with english people? Adopted to the WWW: –XML provides an alphabet and further important means for validation and modularization! –XML does not offer any possibilities to transport conceptual content!

30 13.12.2005 30 RDF: Standard for metadata representation –Basis for interoperability in applications –Cost effective development of tools and applications –Basis for very different users: Digital libraries, content rating, B2B, etc. RDF-Schema: Definition of simple ontologies in the WWW. W3C Recommendation RDF is used by different software companies and standardization organisations RDF – Data Model for the Semantic Web

31 13.12.2005 31 Not the subatomic particle...KArlsruhe Ontology Based on RDF(S), with several extensions, e.g. for typed, multilingual lexical expressions Component-based, easily extendable application framework Open-Source Tool Suite, supporting KAON – A RDF-based Software Infrastructure

32 13.12.2005 32 KAON Architecture RDF Files P2P Relational Database Relational Database NLP Service Applications & Services Web Application Framework HTMLBrowser Ontology and Metadata Editing Reverse Engineering SYNDICATION KAON Portal Portal Maker OntoMat App Framework Focused Crawler Text Mining Evolution Legacy Portals KAON-API RDF-API K-Edutella Wrapper KAON-Server J2EE Middleware NLP-API QEL- Wrapper NLP-API Reasoning Service DOC-API Doc-Manag. Service Data And Remote Services

33 13.12.2005 33 Ontology Engineering Plugin - SOEP …

34 13.12.2005 34 Database Reverse Engineering Plugin - REVERSE …

35 13.12.2005 35 Text Extraction Plugin …

36 13.12.2005 36 Focused Document/Metadata Crawling Plugin …

37 13.12.2005 37 Further Plugins 13.12.2005 Automatic Ontology Extraction Component - TextToOnto Ontology-based Document Clustering Hierarchical Text classification – Automatic Yahoo generation View definition component Peer-2-Peer-based document annotation and authoring (for HTML, PDF, JPEG, GIF) Graphical Query Interface based on QEL SVG-based visualization Versioning component

38 13.12.2005 38 KAON Portal 13.12.2005 KAON Portal is a set of tools supporting ontology-based web site management It supports web-based presentation of information for users (generated and extracted by other components) It also provides means for defining information (cooperatively!)

39 13.12.2005 39 Rapid Prototyping a Semantic Portal KAON Engineering Frontend KAON User Rapid Prototyp Frontend KAON Backend KAON Server

40 13.12.2005 40 Finally: What is behind ? KAON Server! Middleware connects applications with data and network services Generic API’s for –Access to ontologies and metadata –Access to documents –Access to language processing tools –P2P Access API‘s are implemented, e.g. by Stanfords RDF-API, by J2EE complient implementation,etc. Connectors JXTA HTTP, IIOP WebDAV Java API   Security AuthorizationAuthentication Encryption Auditing  Management TransactionReplication Naming Services Storage Data Access Query Update ValidatationInferencing External Services TP MonitorsDatabases Inference Engines  

41 13.12.2005 41 Summarization KAON KAON is basis for approx. 10 research and industry projects. It is also used by external projects all over the world. Open Source Community is growing, currently 35 persons. KAON is basis for building knowledge-intensive and semantics-based applications.

42 13.12.2005 42 Agenda 13.12.2005 Introduction & Motivation Introduction - Semantic Web Semantic Web Applications Semantic Web Technology Next Steps

43 13.12.2005 43 Conclusion We are on the way to a global information structure, being based on the World Wide Web and it‘s successor Semantic Web The main vision is: Support machine-processable and interpretable data to provide a higher degree of automatization (e.g. Web Services, Query Answering, etc.) Standards and tools for ontologies and metadata are ready to use!

44 13.12.2005 44 Next Steps Semantic Web technology should be the basis for the Agricultural Ontology Service (AOS) KAON already provides ready-to-run, open-source tools on which the specific AOS functionalities may be built! Rapid prototyping approach is promising: Convert AGROVOC in RDF(S), connect it with existing data sources and present the information in the Web browser!


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