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Relational Data Mining Applied to Virtual Engineering

of Product Designs

Monika Žáková1, Filip Železný1, Javier A. Garcia-Sedano2,

Cyril Masia Tissot2 and Nada Lavrač3,4

1Department of Cybernetics, CTU Prague,2Semantic Systems, Derio, Spain,

3Jozef Stefan Institute, Ljubljana, Slovenia4University of Nova Gorica, Nova Gorica, Slovenia

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OutlineOutline

1. Motivation

2. Semantic Virtual Engineering

3. Annotation of CAD designs

4. Challenges for ILP

5. Our approach

6. Preliminary results

7. Conclusions and future work

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MotivationMotivation Engineering is one of the most knowledge-intensive

activities

Knowledge in form of CAD designs, documents, simulation models and ERP data bases

Goal: Making implicit knowledge contained in CAD designs explicit useful for reuse, training, quality control

No industrial software employing ILP techniques in real-life regular use we are aware of

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ProjectProject

More specific motivation:

SEVENPRO:

Semantic Virtual Engineering for Product Design project IST-027473(2006-2008) funded under 6th Framework Programme of the European Commission.

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Semantic EngineeringSemantic Engineering

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Design ExampleDesign Example

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Design Design AAnnotationnnotation the information available in CAD files and other

data sources formalized and integrated by means of semantic annotation based on ontologies

Semantic annotation of CAD designs generated automatically from the commands history available via

the API of CAD tools based on a CAD ontology developed in SEVENPRO available in RDF format

annotation including ontology of CAD items and axioms defining core relations automatically translated into Prolog

 

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Annotation ExampleAnnotation Example

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Challenges to ILPChallenges to ILP

There are three main challenges for ILP due to ontolgies in the background knowledge:

hierarchies of term sorts induced by subclassOf relation

hierarchies of relations induced by subpropertyOf relation

representation conversion between Prolog and other knowledge representation languages (SWRL)

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Our Our Baseline Baseline ApproachApproach based on sorted refinement operator (Frisch 1999)

sorted subsumption relation combines θ-subsumption with taxonomies on terms

Tasks:

Currently: Propositionalization Finding maximal patterns Clustering of designs

Other: Classification Requirement/design matching Outlier detection

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RDM System OverviewRDM System Overview

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PropositionalizationPropositionalization propositionalized representation of classified

relational data generated by constructing first-order features

during the feature generation a table of mutual feature subsumptions maintained

this subsumption is exploited in propositional search pruning any conjunctions of subsumer with its subsumee specializing a conjunction not only by extending it, but also by

replacing an included feature with its subsumee.

 

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Finding maximal patternsFinding maximal patterns for non-classified data

maximal patterns

emerging patterns of some limited length covering the minimum set amount of examples

can be used for: Discovering repetitive patterns Finding typical ways some type of item is designed Creating templates that can be reused

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PreliminaryPreliminary ResultsResults the system tested on a set of 35 CAD designs

one design ~ 100 predicates

Language bias imposed based on maximum depth and max. number of relations with the

same input variable the dataset

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Extracted FeaturesExtracted Features Examples of extracted features

f(X1:cADFileRevision) = hasCADEntity(X1:cADFileRevision,X2:cADPart), hasBody(X2:cADPart,X3:body),hasFeature(X3:body,X4:extrude),…, hasFeature(X3:body,X7:extrude),hasFeature(X3:body,X8:pocket),...hasFeature(X3:body,X12:pocket),hasFeature(X3:body,X13:fillet),... hasFeature(X3:body, X16:fillet), hasFeature(X3:body,X17:cADFeature).

f(X1:cADFileRevision) = hasCADEntity(X1:cADFileRevision,X2:cADPart), hasBody(X2:cADPart,X3:body),hasFeature(X3:body,X4:extrude),…,hasFeature(X3:body,X7:extrude),hasSketch(X7:extrude,X8:circularSketch),hasGeomElement(X8:circularSketch,X9:circle).

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Future workFuture work Include taxonomy on predicates

Improve efficiency using graph search techniques

For closer integration of more complex hierarchical background knowledge the following approaches considered Integration of subsumption operator with proven properties Use of hybrid languages

AL-log, CARIN

Use of more complex representational formalism ψ-terms, antecedent description grammars

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Thank you

for your attention