1. Knowledge Mediation in the WWW based on Labelled DAGs with Attached Constraints Jutta Eusterbrock WebTechnology GmbH

2. Introduction WWW: Vast amount of useful data and information in online repositories, electronic product catalogues,... for configuration, planning, synthesis applications Problems: –WWW topology is dynamic, content changes quickly –Information from various locations differs in syntax, structure, semantics –Domain-specific meta-knowledge and interacting constraints need to be taken into account

3. Introduction Goal: Use of WWW data from various locations, attached meta-knowledge and constraints for applications, eg. configuration, based on reasoning and constraint-solving Approach –Agent Framework –Viewpoint as Mediator (Intermediate layer between resources and applications) –Logical Representations of XML-Graphs for Data Integration

4. Knowledge Integration XML DTD XML (eXtensible Mark-up Language) –Emerging standard for exchanging data on the WWW –Objects consist of nested elements,attribute/value pairs DTD (Document Type Descriptor) –Grammar, Vocabulary (optional)

5. Knowledge Integration XML Query Language Access to fragments of XML elements through a number of query languages, based on path- expressions –Example in XML-QL [Deutsch, Fernandez, Florescu, Levy, Suciu, 98]

6. Knowledge Integration Agents Application of the KRAFT Agent Framework –Software components realised as interacting agents –Subset of KQML performatives for communication –Facilitators encompass descriptions of service providers that have to advertise their capabilities –Wrappers translate and distribute queries –Mediators provide uniform access to heterogeneous information resources

7. Knowledge Integration Knowledge-Bases Shared Ontology –Formal semantic domain model –Explicit specification of agreed, standardised vocabulary, definition of the basic terms (concepts), properties, relationships ( Gruber ) and background knowledge Facilitator Knowledge Base –Representations of syntactic Web (meta-) data, schemas, locations XML elements, DTDs Stored as facts in KB

8. Knowledge Integration Viewpoint Realisation of Mediators by Viewpoints –Provide context-specific definitions for the ontological concepts –Based on lifting rules (articulation axioms, Guha, Cyc ) –Interpretation with respect to a semantic requests generates a set of syntactic queries to individual resources by reasoning, constraint-solving –Knowledge Integration with respect to a given ontology

9. Knowledge Integration on the WWW

10. Graphs for Web Knowledge Bases Wrapping, storing, retrieval, reasoning based on a logical representation of graphs –Labelled DAGs as data model for XML elements –Feature Graphs for modeling DTDs and concepts –ADT for labelled DAGs and efficient canonical term encodings ( Eusterbrock, 97) Graph retrieval based on path-expressions Graph matching modulo isomorphism by term matching Graph term subsumption models class-, instance relations

11. Graphs for Web KBs XML Data Model

12. Graphs for Web KB: Term Encoding Example: Canonical term encoding of XML element and DTD

13. Domain Ontology with Constraints Representation of domain concepts by feature graphs with attached constraints

14. Knowledge-Based Mediation: Objective Queries can be expressed –using the vocabulary of a shared ontology –built-up as conjunction of atoms, graph-path expressions and constraints attached to free variables –without having to take into account location

15. Knowledge-Based Mediation: Sharing Local Domain Models: Facts based on DTDs Ontology: CLPs with embedded feature graphs for concepts Integration: Translation DTD  concept Semantic mismatches still need to be resolved! –missing, overlapping features –feature semantic: prices before/after taxes –domain values: measurement of units, dimensions

16. Sharing Rules: AtomicConcept <= Constraints /\ DTD –Examples: Specialisation, Unit Conversion Knowledge-Based Mediation: Sharing

17. Sharing Rules: AtomicConcept <= Constraints /\ DTD –AtomicConcept DTD all kinds of graph mappings, e.g. renaming, projection, Lifting –Causal relation between a common aggregated concept and the set of associated local context (DTDs) –ComposedConcept(_,Subconcept1,...,Subconceptn) <= FusedConstraints /\ DTD1 /\... /\ DTDn –Linearisation of composed concepts

18. Knowledge-based Mediation: (Automatic) synthesis of lifting rules –graph rewriting, constraint fusion, using sharing rules

20. Knowledge-based Mediation: Method Transformation: Selecting concepts that match graph-paths, rewriting query, using ADT graph Decomposition of logical query into queries to individual resources and composition of results –Interpreting lifting rules, background knowledge by reasoning, constraint-solving yields atomic queries –Locating suitable resources using facilitator KB –Wrapping, distributing, querying XML resources –Integrating returned results into CLP. Non- satisfiability causes backtracking.

21. Discussion ( Some Related Work ) Mediator systems –KOMET,HERMES logic, deduction for mediator –Focus largely on uniform access to DBs Ontology-based semantic access –ONTOBROKER Framelogic for encoding of ontologies Generation of DTDs directly from ontologies

22. Discussion ( New Results) Lifting rules and viewpoints: –Flexible framework for loose integration Graph encoding of XML data, DTDs and concepts –Natural models for knowledge sharing –Construction of wrappers is straightforward –Canonical term encodings provide efficient procedures Constraints as meta-knowledge –Essential for the automatisation of design tasks –Rewriting, constraint-solving is automated