© 2002 DFKI GmbH

CCSW:The Competence Center Semantic Web

Harold Boley, DFKI GmbH

Presentation in Course „Rule Markup Languages“

Univ. Kaiserslautern, April 26th, 2002

2© 2002 DFKI GmbH

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 (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)

URL: http://ccsw.dfki.deServices: Consulting, Studies & Projects

3© 2002 DFKI GmbH

Semantic Web and Web ServicesUse 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

4© 2002 DFKI GmbH

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

5© 2002 DFKI GmbH

Some SemWeb Applications@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 fromMultilingual Sources

6© 2002 DFKI GmbH

Some SemWeb Applications@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

FRODO Ontology Acquisition from Texts and User Interaction

for Workflow Enactment and Information Access

7© 2002 DFKI GmbH

The Semantic Web Layered Architecture

(http://www.w3.org/2001/Talks/0228-tbl/slide5-0.html)

Tim Berners-Lee:“Axioms, Architecture and Aspirations”W3C all-working group plenary Meeting28 February 2001

8© 2002 DFKI GmbH

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, eCl@ss , ECCnet)?

• How to rank candidate semweb applications for showing the semweb potentials in our own organizations and for our customers?

9© 2002 DFKI GmbH

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© 2002 DFKI GmbH

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© 2002 DFKI GmbH

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© 2002 DFKI GmbH

Merchant1 Merchantm

. . .

Customer or Company

publishrulebase1

publishrulebasem

compare, instantiate,and run rulebases

Web-Based B2C or B2B Rule Exchange

translate tostandard format(e.g., RuleML)

13© 2002 DFKI GmbH

From Natural Language to Horn Logic

Prolog-like formalization (syntax generated from XML):

''The discount for a customer buying a product is 5.0 percentif the customer is premium and the product is regular.''''The discount for a customer buying a product is 7.5 percentif the customer is premium and the product is luxury.''. . .

English Business Rules:

14© 2002 DFKI GmbH

RuleML: Markup and Tree''The discount for a customer buying a product is 5.0 percentif 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>

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

15© 2002 DFKI GmbH

Intertranslating RuleML and RFML''The discount for a customer buying a product is 5.0 percentif 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>

ruleml2rfml.xsl

rfml2ruleml.xsl

16© 2002 DFKI GmbH

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© 2002 DFKI GmbH

Major Funding• USA: DAML, W3C Web Ontology Working

Group• Canada: NRC

• Europe: OntoWeb, Semantic Web Technologies

• Japan: METI

• . . .

• Canada + Europe: ISTEC

• Japan + Europe: ?

• . . .

18© 2002 DFKI GmbH

SemWeb Courses• University of Maryland

• Stanford University

• Lehigh University

• Vrije Universiteit Amsterdam

• Universität Karlsruhe

• Universität Kaiserslautern

• Universität Saarbrücken

• ...