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CCSW : The C ompetence C enter S emantic W eb

CCSW : The C ompetence C enter S emantic W eb. Harold Boley, DFKI GmbH Presentation in Course „Rule Markup Languages“ Univ. Kaiserslautern, April 26th, 2002. General Overview.

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CCSW : The C ompetence C enter S emantic W eb

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  1. CCSW:The Competence Center Semantic Web Harold Boley, DFKI GmbH Presentation in Course „Rule Markup Languages“ Univ. Kaiserslautern, April 26th, 2002

  2. General Overview • Semantic Web: W3C Activity on machine-interpreted documents that can be used (not just for display but) for automation, integration, and reuseacross applications(http://www.w3.org/2001/sw/#activity) • DFKI has long been working in Semantic Web technologies:Description logics, ontologies, metadata, rule systems, agents,NL parsing, information extraction, knowledge management, etc. • Current CCSW focus at DFKI:Robust Web-document authoring & annotation for agent-based information management with webizedobject representations, ontologies & rule systems • CCSW‘s Semantic Web view: Higher-level system emerging from increasingly structured subwebs, each serving needs of specific community Co-Heads: Dr. Harold Boley (Kaiserslautern), Dr. Paul Buitelaar (Saarbrücken) Services: Consulting, Studies & Projects URL:http://ccsw.dfki.de

  3. Semantic Web: DAML+OIL Web Services : WSDL Category-Based Search Engines & Document Retrieval Formal Ontologies & Metadata Repositories First-Order Logic & Knowledge Representation Mediator Agents & Information Integration Interface Descriptions & CGI Scripts Communication Protocols & Remote Procedure Calls Databases: SQL Rule Systems: RuleML (Integration of) Schemas & Dictionaries (Distributed) Transaction Processing Triggers & Events Derivation Rules Transformation Rules Reaction Rules Semantic Web and Web ServicesUse Databases and Rule Systems

  4. Infrastructure: Web Ontology-Based KR Languages • Taxonomies/Description Logics  Axioms/Rules/Inference (RuleML) Ontologies General DFKI SemWeb Areas • Content: Ontology Development • Manual, Semi-Automatic Ontology Learning and Adaptation • Specific for a Task, Organisation (IntraNet), Domain (ExtraNet) • Applications: Intelligent and Dynamic Information Integration and Access • Intelligent Information Integration • Intelligent, Cooperative Agents • Content-Based Information Access • Cross-Lingual and Multimedia Information Access • Company- and User-Adaptive Information Systems • Distributed Agent-Based Organizational Memories

  5. Some SemWebApplications@DFKI (I) Intelligent Information Integration & Intelligent, Cooperative Agents SmartKOM Combination of User Modeling and Plan Recognition to Integrate Knowledge from Multimodal Sources Intelligent Information Integration MUMIS Ontology-Based Information Integration from Multilingual Sources Content-Based, Cross-Lingual & Multimedia Information Access Combinations of Ontology-Based Information Extraction, Text Mining and Semantic Annotation for Knowledge Markup of Text or Multimedia Documents with Metadata for Content-Based, Cross-Lingual, Multimedia Information Access GETESS (Information Extraction, Text Mining), MuchMore (Semantic Annotation, Text Mining), MUMIS (Information Extraction, Multimedia)

  6. Some SemWebApplications@DFKI (II) Company- and User-Adaptive Information Systems Adaptive READDocument Retrieval on the Basis of Machine Learning Algorithms for Automatic IR-Parameter Optimization Distributed Agent-Based Organizational Memories FRODO Ontology Acquisition from Texts and User Interaction for Workflow Enactment and Information Access

  7. The Semantic Web Layered Architecture Tim Berners-Lee: “Axioms, Architecture and Aspirations” W3C all-working group plenary Meeting 28 February 2001 (http://www.w3.org/2001/Talks/0228-tbl/slide5-0.html)

  8. Present SemWeb Challenges • Can we make W3C’s original “Semantic Web” notion more • precise (“Semantic”): content data vs. metadata semantics? • specific (“Web”): someintranets vs. the Internet? • What techniques will “semantic webs” use from Information Retrieval, Databases, Ontologies, (Description, Horn)Logics, W3C Markup Languages (XML, RDF, XSLT), Knowledge Management, Agents, Web Services (WSDL), ...? • Which semweb success stories (“killer apps”) exist (dmoz.org; UNSPSC, eCl@ss , ECCnet)? • How to rank candidate semweb applications for showing the semweb potentials in our own organizations and for our customers?

  9. SemWeb Language Principles • Existing (database, logic) languages can be “webized” (Tim Berners-Lee) by introducing URIs as a new kind of (constant) symbols • The languages should be scalable to a large amount of Web-distributed content, hence should use a small, if not minimal, formalism: • A simple formalism doesn’t interfere with the content • Relational databases with SQL are a good example • XML DTDs, the RDF model, the DAML+OIL core, and the modularized RuleML are such candidate languages (unlike, perhaps, XML Schema, the many RDF syntaxes, full DAML+OIL, or a monolithic RuleML)

  10. SemWeb Core Issue:Metadata Ontologies (I) • For Web-page annotation, browsers should use a top-level pane/menu for metadata (cf. Annotea) • Metadata should be generated interactively from content data, via standardized domain ontologies (NLP tools/resources for metadata extraction & annotation) • Search engines should show same ontologies for navigating-searching content with high precision • Information agents may also use the ontologies for retrieving and integrating content for users

  11. SemWeb Core Issue:Metadata Ontologies (II) • Instead of a single “global ontology” for metadata there will certainly be several “local ontologies”, which require integration, e.g. by alignment on demand or via derivation/transformation rules • Maintenance of domain ontologies for metadata must be machine-supported, e.g. by links and/or transformations between versions (cf. MeSH) • Metadata ontologies can describe heterogeneous Web pages in a homogeneous format • Some ontology queries provide direct answers (‘fact retrieval’); others provide relevant Web pages (‘document retrieval’); yet others, both

  12. Merchant1 Merchantm Customer or Company Web-Based B2CorB2B Rule Exchange . . . translate to standard format (e.g., RuleML) publish rulebase1 publish rulebasem compare, instantiate, and run rulebases

  13. English Business Rules: ''The discount for a customer buying a product is 5.0 percent if the customer is premium and the product is regular.'' ''The discount for a customer buying a product is 7.5 percent if the customer is premium and the product is luxury.'' . . . Prolog-like formalization (syntax generated from XML): From Natural Language to Horn Logic

  14. imp head atom opr rel discount var customer var product ind 5.0 percent body and atom opr rel premium var customer atom opr rel regular var product RuleML: Markup and Tree ''The discount for a customer buying a product is 5.0 percent if the customer is premium and the product is regular.'' <imp> <_head> <atom> <_opr><rel>discount</rel></_opr> <var>customer</var> <var>product</var> <ind>5.0 percent</ind> </atom> </_head> <_body> <and> <atom> <_opr><rel>premium</rel></_opr> <var>customer</var> </atom> <atom> <_opr><rel>regular</rel></_opr> <var>product</var> </atom> </and> </_body> </imp>

  15. ruleml2rfml.xsl rfml2ruleml.xsl Intertranslating RuleML and RFML ''The discount for a customer buying a product is 5.0 percent if the customer is premium and the product is regular.'' <imp> <_head> <atom> <_opr><rel>discount</rel></_opr> <var>customer</var> <var>product</var> <ind>5.0 percent</ind> </atom> </_head> <_body> <and> <atom> <_opr><rel>premium</rel></_opr> <var>customer</var> </atom> <atom> <_opr><rel>regular</rel></_opr> <var>product</var> </atom> </and> </_body> </imp> <hn> <pattop> <con>discount</con> <var>customer</var> <var>product</var> <con>5.0 percent</con> </pattop> <callop> <con>premium</con> <var>customer</var> </callop> <callop> <con>regular</con> <var>product</var> </callop> </hn>

  16. Joint Committee Current Players • USA: W3C, DARPA, NSF, Maryland, Stanford, ... • Canada: NRC-IIT-CISTI, ... • Europe: IST • Netherlands: Amsterdam, Twente, ... • UK: Manchester, Newcastle, ... • France: INRIA , ... • Germany: Karlsruhe, DFKI, Hannover, Hamburg, Berlin, IW-Köln, ... • Sweden: Linköping • Switzerland: MCM • Japan: INTAP, Keio, CARC, Ricoh, ... • Korea: KAIST • Australia: Melbourne, ... • . . .

  17. Major Funding • USA: DAML, W3C Web Ontology Working Group • Canada: NRC • Europe: OntoWeb, Semantic Web Technologies • Japan: METI • . . . • Canada + Europe: ISTEC • Japan + Europe: ? • . . .

  18. SemWeb Courses • University of Maryland • Stanford University • Lehigh University • Vrije Universiteit Amsterdam • Universität Karlsruhe • Universität Kaiserslautern • Universität Saarbrücken • ...

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