1. 1 Relational Data Mining Applied to Virtual Engineering of Product Designs Monika Žáková 1, Filip Železný 1, Javier A. Garcia-Sedano 2, Cyril Masia Tissot 2 and Nada Lavrač 3,4 1 Department of Cybernetics, CTU Prague, 2 Semantic Systems, Derio, Spain, 3 Jozef Stefan Institute, Ljubljana, Slovenia 4 University of Nova Gorica, Nova Gorica, Slovenia

2. ILP 2006 2 / 17 Outline 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

3. ILP 2006 3 / 17 Motivation 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

4. ILP 2006 4 / 17 Project More specific motivation: SEVENPRO: Semantic Virtual Engineering for Product Design project IST-027473(2006-2008) funded under 6th Framework Programme of the European Commission.

5. ILP 2006 5 / 17 Semantic Engineering

6. ILP 2006 6 / 17 Design Example

7. ILP 2006 7 / 17 Design Annotation 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

8. ILP 2006 8 / 17 Annotation Example

9. ILP 2006 9 / 17 Challenges 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)

10. ILP 2006 10 / 17 Our Baseline Approach Our Baseline Approach 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

11. ILP 2006 11 / 17 RDM System Overview

12. ILP 2006 12 / 17 Propositionalization 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.

13. ILP 2006 13 / 17 Finding 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

14. ILP 2006 14 / 17 Preliminary Results Preliminary Results 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

15. ILP 2006 15 / 17 Extracted 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:circular Sketch),hasGeomElement(X8:circularSketch,X9:circle).

16. ILP 2006 16 / 17 Future 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

17. ILP 2006 17 / 17 Thank you for your attention