Inductive logic programming : 9th International Workshop, ILP-99, Bled, Slovenia, June 24-27, 1999 : proceedings
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Inductive logic programming : 9th International Workshop, ILP-99, Bled, Slovenia, June 24-27, 1999 : proceedings
（Lecture notes in computer science, 1634 . Lecture notes in artificial intelligence）
Springer, c1999
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Description and Table of Contents
Description
Thisvolumecontains3invitedand24submittedpaperspresentedattheNinth InternationalWorkshoponInductiveLogicProgramming,ILP-99. The24acc- tedpaperswereselectedbytheprogramcommitteefromthe40paperssubmitted toILP-99. Eachpaperwasreviewedbythreereferees,applyinghighreviewing standards. ILP-99washeldinBled,Slovenia,24{27June1999. Itwascollocatedwith theSixteenthInternationalConferenceonMachineLearning,ICML-99,held27{ 30June1999. On27June,ILP-99andICML-99weregivenajointinvitedtalk byJ. RossQuinlanandajointpostersessionwhereallthepapersacceptedat ILP-99andICML-99werepresented. TheproceedingsofICML-99(editedby IvanBratkoandSa soD zeroski)arepublishedbyMorganKaufmann. WewishtothankalltheauthorswhosubmittedtheirpaperstoILP-99,the programcommitteemembersandotherreviewersfortheirhelpinselectinga high-qualityprogram,andtheinvitedspeakers:DaphneKoller,HeikkiMannila, andJ. RossQuinlan. ThanksareduetoTanjaUrban ci candherteamandMajda Zidanskiandherteamfortheorganizationalsupportprovided. Wewishtothank AlfredHofmannandAnnaKramerofSpringer-Verlagfortheircooperationin publishing these proceedings. Finally, we gratefully acknowledge the nancial supportprovidedbythesponsorsofILP-99.
April1999 Sa soD zeroski PeterFlach ILP-99ProgramCommittee FrancescoBergadano(UniversityofTorino) HenrikBostr..om(UniversityofStockholm) IvanBratko(UniversityofLjubljana) WilliamCohen(AT&TResearchLabs) JamesCussens(UniversityofYork) LucDeRaedt(UniversityofLeuven) Sa soD zeroski(Jo zefStefanInstitute,co-chair) PeterFlach(UniversityofBristol,co-chair) AlanFrisch(UniversityofYork) KoichiFurukawa(KeioUniversity) RoniKhardon(UniversityofEdinburgh) NadaLavra c(Jo zefStefanInstitute) JohnLloyd(AustralianNationalUniversity) StanMatwin(UniversityofOttawa) RaymondMooney(UniversityofTexas) StephenMuggleton(UniversityofYork) Shan-HweiNienhuys-Cheng(UniversityofRotterdam) DavidPage(UniversityofLouisville) BernhardPfahringer(AustrianResearchInstituteforAI) CelineRouveirol(UniversityofParis) ClaudeSammut(UniversityofNewSouthWales) MicheleSebag(EcolePolytechnique) AshwinSrinivasan(UniversityofOxford) PrasadTadepalli(OregonStateUniversity) StefanWrobel(GMDResearchCenterforInformationTechnology) OrganizationalSupport TheAlbatrossCongressTouristAgency,Bled Center for Knowledge Transfer in Information Technologies, Jo zef Stefan Institute,Ljubljana SponsorsofILP-99 ILPnet2,NetworkofExcellenceinInductiveLogicProgramming COMPULOGNet,EuropeanNetworkofExcellenceinComputationalLogic Jo zefStefanInstitute,Ljubljana LPASoftware,Inc.
UniversityofBristol TableofContents I InvitedPapers ProbabilisticRelationalModels D. Koller ...3 InductiveDatabases(Abstract) H. Mannila...14 SomeElementsofMachineLearning(ExtendedAbstract) J. R. Quinlan...15 II ContributedPapers Re nementOperatorsCanBe(Weakly)Perfect L. Badea,M. Stanciu...21 CombiningDivide-and-ConquerandSeparate-and-ConquerforE cientand E ectiveRuleInduction H. Bostr..om,L. Asker...33 Re ningCompleteHypothesesinILP I. Bratko...44 AcquiringGraphicDesignKnowledge withNonmonotonicInductiveLearning K. Chiba,H. Ohwada,F. Mizoguchi...56 MorphosyntacticTaggingofSloveneUsingProgol J. Cussens,S. D zeroski,T. Erjavec ...68 ExperimentsinPredictingBiodegradability S. D zeroski,H. Blockeel,B. Kompare,S. Kramer, B. Pfahringer,W. VanLaer ...80 1BC:AFirst-OrderBayesianClassi er P. Flach,N. Lachiche...92 SortedDownwardRe nement:BuildingBackgroundKnowledge intoaRe nementOperatorforInductiveLogicProgramming A. M. Frisch ...104 AStrongCompleteSchemaforInductiveFunctionalLogicProgramming J. Hern andez-Orallo,M. J. Ram rez-Quintana...116 ApplicationofDi erentLearningMethods toHungarianPart-of-SpeechTagging T. Horv ath,Z. Alexin,T. Gyim othy,S. Wrobel ...1
28 VIII TableofContents CombiningLAPISandWordNetfortheLearningofLRParserswith OptimalSemanticConstraints D. Kazakov...140 LearningWordSegmentationRulesforTagPrediction D. Kazakov,S. Manandhar,T. Erjavec ...152 ApproximateILPRulesbyBackpropagationNeuralNetwork: AResultonThaiCharacterRecognition B. Kijsirikul,S. Sinthupinyo...162 RuleEvaluationMeasures:AUnifyingView N. Lavra c,P. Flach,B. Zupan...174 ImprovingPart-of-SpeechDisambiguationRulesbyAdding LinguisticKnowledge N. Lindberg,M. Eineborg ...186 OnSu cientConditionsforLearnabilityofLogicProgramsfrom PositiveData E. Martin,A. Sharma ...198 ABoundedSearchSpaceofClausalTheories H. Midelfart...210 DiscoveringNewKnowledgefromGraphData UsingInductiveLogicProgramming T. Miyahara,T. Shoudai,T. Uchida,T. Kuboyama, K. Takahashi,H. Ueda...
Table of Contents
I Invited Papers.- Probabilistic Relational Models.- Inductive Databases.- Some Elements of Machine Learning.- II Contributed Papers.- Refinement Operators Can Be (Weakly) Perfect.- Combining Divide-and-Conquer and Separate-and-Conquer for Efficient and Effective Rule Induction.- Refining Complete Hypotheses in ILP.- Acquiring Graphic Design Knowledge with Nonmonotonic Inductive Learning.- Morphosyntactic Tagging of Slovene Using Progol.- Experiments in Predicting Biodegradability.- 1BC: A First-Order Bayesian Classifier.- Sorted Downward Refinement: Building Background Knowledge into a Refinement Operator for Inductive Logic Programming.- A Strong Complete Schema for Inductive Functional Logic Programming.- Application of Different Learning Methods to Hungarian Part-of-Speech Tagging.- Combining LAPIS and WordNet for the Learning of LR Parsers with Optimal Semantic Constraints.- Learning Word Segmentation Rules for Tag Prediction.- Approximate ILP Rules by Backpropagation Neural Network: A Result on Thai Character Recognition.- Rule Evaluation Measures: A Unifying View.- Improving Part of Speech Disambiguation Rules by Adding Linguistic Knowledge.- On Sufficient Conditions for Learnability of Logic Programs from Positive Data.- A Bounded Search Space of Clausal Theories.- Discovering New Knowledge from Graph Data Using Inductive Logic Programming.- Analogical Prediction.- Generalizing Refinement Operators to Learn Prenex Conjunctive Normal Forms.- Theory Recovery.- Instance based function learning.- Some Properties of Inverse Resolution in Normal Logic Programs.- An Assessment of ILP-assisted models for toxicology and the PTE-3 experiment.
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