Knowledge Mediation in the WWW based on Labelled DAGs

with Attached Constraints

Jutta Eusterbrock

WebTechnology GmbH

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

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

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)

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]

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

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

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

Knowledge Integration on the WWW

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

Graphs for Web KBs XML Data Model

Graphs for Web KB: Term Encoding

• Example: Canonical term encoding of XML element and DTD

Domain Ontology with Constraints

• Representation of domain concepts by feature graphs with attached constraints

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

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

• Sharing Rules: AtomicConcept <= Constraints /\ DTD

– Examples: Specialisation, Unit Conversion

Knowledge-Based Mediation: Sharing

Knowledge-Based Mediation: Sharing

• 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

Knowledge-based Mediation:

• (Automatic) synthesis of lifting rules– graph rewriting, constraint fusion, using sharing rules

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.

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

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