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

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