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

© 2002 DFKI GmbH 1 General Overview Semantic Web: W3C Activity on machine-interpreted documents that can be used (not just for display but) for automation, integration, and reuse across applications ( 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 webized object 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) URL: Consulting, Studies & Projects

© 2002 DFKI GmbH 2 Semantic Web and Web Services Use Databases and Rule Systems Databases: SQL (Integration of) Schemas & Dictionaries (Distributed) Transaction Processing Triggers & Events Rule Systems: RuleML Derivation Rules Transformation Rules Reaction Rules Category-Based Search Engines & Document Retrieval Formal Ontologies & Metadata Repositories First-Order Logic & Knowledge Representation Semantic Web: DAML+OIL Mediator Agents & Information Integration Interface Descriptions & CGI Scripts Communication Protocols & Remote Procedure Calls Web Services : WSDL

© 2002 DFKI GmbH 3 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 Infrastructure: Web Ontology-Based KR Languages  Taxonomies/Description Logics  Axioms/Rules/Inference (RuleML) Ontologies

© 2002 DFKI GmbH 4 Some SemWeb DFKI (I) 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) 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

© 2002 DFKI GmbH 5 Some SemWeb DFKI (II) Company- and User-Adaptive Information Systems Adaptive READ Document Retrieval on the Basis of Machine Learning Algorithms for Automatic IR-Parameter Optimization Distributed Agent-Based Organizational Memories FRODOOntology Acquisition from Texts and User Interaction for Workflow Enactment and Information Access

© 2002 DFKI GmbH 6 The Semantic Web Layered Architecture ( Tim Berners-Lee: “Axioms, Architecture and Aspirations” W3C all-working group plenary Meeting 28 February 2001

© 2002 DFKI GmbH 7 Present SemWeb Challenges Can we make W3C’s original “Semantic Web” notion more –precise (“Semantic”): content data vs. metadata semantics? –specific (“Web”): some intranets 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, ECCnet How to rank candidate semweb applications for showing the semweb potentials in our own organizations and for our customers?

© 2002 DFKI GmbH 8 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)

© 2002 DFKI GmbH 9 SemWeb Core Issue: Metadata Ontologies (I) For Web-page annotation, browsers should use a top-level pane/menu for metadata (cf. Annotea)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

© 2002 DFKI GmbH 10 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)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

© 2002 DFKI GmbH 11 Merchant 1 Merchant m... Customer or Company publish rulebase 1 publish rulebase m compare, instantiate, and run rulebases Web-Based B2C or B2B Rule Exchange translate to standard format (e.g., RuleML)

© 2002 DFKI GmbH 12 From Natural Language to Horn Logic Prolog-like formalization (syntax generated from XML): ''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.''... English Business Rules:

© 2002 DFKI GmbH 13 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.'' discount customer product 5.0 percent premium customer regular product 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

© 2002 DFKI GmbH 14 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.'' discount customer product 5.0 percent premium customer regular product discount customer product 5.0 percent premium customer regular product ruleml2rfml.xsl rfml2ruleml.xsl

© 2002 DFKI GmbH 15 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,......

© 2002 DFKI GmbH 16 Major Funding USA: DAML, W3C Web Ontology Working Group Canada: NRC Europe: OntoWeb, Semantic Web Technologies Japan: METI... Canada + Europe: ISTEC Japan + Europe: ?...

© 2002 DFKI GmbH 17 SemWeb Courses University of Maryland Stanford University Lehigh University Vrije Universiteit Amsterdam Universität Karlsruhe Universität Kaiserslautern Universität Saarbrücken...