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.