Knowledge Acquisition from Distributed, Autonomous, Semantically Heterogeneous Data and Knowledge Sources (KADASH)

ion. For example, the program of study a student in a data source can be specified as Graduate Program (higher level of abstraction), while the program of study of a different student in the same data source (or even a different data source) can be specified as Doctoral Program (lower level of abstraction). 2005 IEEE ICDM Workshop on KADASH 5 The workshop brings together researchers in relevant areas of artificial intelligence (machine learning, data mining, knowledge representation, ontologies), information systems (information integration, databases, semantic Web), distributed computing (service-oriented computing) and selected application areas (e.g., bioinformatics, security informatics, environmental informatics) to address several questions such as: • What are some of the research challenges presented by emerging data-rich application domains such as bioinformatics, health informatics, security informatics, social informatics, environmental informatics? • How can we perform knowledge discovery from distributed data (assuming different types of data fragmentation, e.g., horizontal or vertical data fragmentation; different hypothesis classes, e.g., naïve Bayes, decision tree, support vector machine classifiers; different performance criteria, e.g., accuracy versus complexity versus reliability of the model generated, etc.)? • How can we make semantically heterogeneous data sources self-describing (e.g., by explicitly associating ontologies with data sources and mappings between them) in order to help collaborative scientific discovery from autonomous information sources? • How can we represent, manipulate, and reason with ontologies and mappings between