Discovery Science

This volume contains the papers presented at the 8th International Conference onDiscoveryScience(DS2005)heldinSingapore, RepublicofSingapore, during the days from 8 11 of October 2005. The main objective of the Discovery Science (DS) conference series is to p- vide an open forum for intensive discussions and the exchange of new ideas and information among researchers working in the area of automating scienti? c d- covery or working on tools for supporting the human process of discovery in science. It has been a successful arrangement in the past to co-locate the DS conference with the International Conference on Algorithmic Learning Theory (ALT). This combination of ALT and DS allows for a comprehensive treatment ofthewholerange, fromtheoreticalinvestigationstopracticalapplications. C- tinuing in this tradition, DS 2005 was co-located with the 16th ALT conference (ALT2005). TheproceedingsofALT 2005werepublished asa twinvolume3734 of the LNCS series. TheInternationalSteeringCommitteeoftheDiscoveryScienceconference- ries providedimportantadviceon a number ofissues during the planning of D- coveryScience2005. ThemembersoftheSteeringCommiteeareHiroshiMotoda, (Osaka University), Alberto Apostolico (Purdue University), Setsuo Arikawa (Kyushu University), Achim Ho? mann (University of New South Wales), Klaus P. Jantke (DFKI and FIT Leipzig, Germany), Massimo Melucci (U- versityofPadua), Masahiko Sato(Kyoto University), Ayumi Shinohara(Tohoku University), EinoshinSuzuki(YokohamaNationalUniversity), andThomasZe- mann (Hokkaido University). We received 112 full paper submissions out of which 21 long papers (up to 15 pages), 7 regular papers (up to 9 pages), and 9 project reports (3 pages) were acceptedforpresentationandarepublished inthis volume. Eachsubmissionwas reviewed by at least two members of the Program Committee of international expertsinthe? eld. Theselectionwasmadeaftercarefulevaluationofeachpaper based on originality, technical quality, relevance to the ? eld of discovery science, and clarity.

Invited Papers. - Invention and Artificial Intelligence. - Algorithms and Software for Collaborative Discovery from Autonomous, Semantically Heterogeneous, Distributed Information Sources. - Training Support Vector Machines via SMO-Type Decomposition Methods. - The Robot Scientist Project. - The Arrowsmith Project: 2005 Status Report. - Regular Contributions - Long Papers. - Practical Algorithms for Pattern Based Linear Regression. - Named Entity Recognition for the Indonesian Language: Combining Contextual, Morphological and Part-of-Speech Features into a Knowledge Engineering Approach. - Bias Management of Bayesian Network Classifiers. - A Bare Bones Approach to Literature-Based Discovery: An Analysis of the Raynaud s/Fish-Oil and Migraine-Magnesium Discoveries in Semantic Space. - Assisting Scientific Discovery with an Adaptive Problem Solver. - Cross-Language Mining for Acronyms and Their Completions from the Web. - Mining Frequent ? -Free Patterns in Large Databases. - An Experiment with Association Rules and Classification: Post-Bagging and Conviction. - Movement Analysis of Medaka (Oryzias Latipes) for an Insecticide Using Decision Tree. - Support Vector Inductive Logic Programming. - Measuring Over-Generalization in the Minimal Multiple Generalizations of Biosequences. - The q-Gram Distance for Ordered Unlabeled Trees. - Monotone Classification by Function Decomposition. - Learning On-Line Classification via Decorrelated LMS Algorithm: Application to Brain-Computer Interfaces. - An Algorithm for Mining Implicit Itemset Pairs Based on Differences of Correlations. - Pattern Classification via Single Spheres. - SCALETRACK: A System to Discover Dynamic Law Equations Containing Hidden States and Chaos. - Exploring Predicate-Argument Relations for Named Entity Recognition in the MolecularBiology Domain. - Massive Biomedical Term Discovery. - Active Constrained Clustering by Examining Spectral Eigenvectors. - Learning Ontology-Aware Classifiers. - Regular Contributions - Regular Papers. - Automatic Extraction of Proteins and Their Interactions from Biological Text. - A Data Analysis Approach for Evaluating the Behavior of Interestingness Measures. - Unit Volume Based Distributed Clustering Using Probabilistic Mixture Model. - Finding Significant Web Pages with Lower Ranks by Pseudo-Clique Search. - CLASSIC CL: An Integrated ILP System. - Detecting and Revising Misclassifications Using ILP. - Project Reports. - Self-generation of Control Rules Using Hierarchical and Nonhierarchical Clustering for Coagulant Control of Water Treatment Plants. - A Semantic Enrichment of Data Tables Applied to Food Risk Assessment. - Knowledge Discovery Through Composited Visualization, Navigation and Retrieval. - A Tabu Clustering Method with DHB Operation and Mergence and Partition Operation. - Discovering User Preferences by Using Time Entries in Click-Through Data to Improve Search Engine Results. - Network Boosting for BCI Applications. - Rule-Based FCM: A Relational Mapping Model. - Effective Classifier Pruning with Rule Information. - Text Mining for Clinical Chinese Herbal Medical Knowledge Discovery.