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AAAI Workshop
Summer 2000
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Researchers from a variety of backgrounds (including
machine learning, statistics, inductive logic programming, databases, and
reasoning under uncertainty) are beginning to develop techniques to learn
statistical models from relational data. This work diverges from traditional
approaches in these fields that assume data instances are structurally
identical and statistically independent or assume that relationships are
deterministic. New developments in this area are vital because of the growing
interest in mining information in relational databases, object-oriented
databases, XML and other structured and semi-structured formats. This Website
focuses on techniques that operate directly on relational data to learn
models that represent statistical correlations among the properties of
related entities. It grew out of a workshop
at AAAI 2000.
Specifically, we focus on:
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Methods for learning statistical models from heterogeneous, non-independent
samples
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Non-propositional data representations (including relational and first-order
models)
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Efficient techniques for mining relational and semi-structured data
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Applications of relational data analysis (e.g., Web mining, counter-terrorism,
intrusion detection, collaborative filtering, bioinformatics)
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