Pattern mining from linked data, methods, tools and applications

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This program is about extracting useful knowledge from the structured data that is published on the Web. It researches the adaptation of previously contributed methods and techniques for pattern mining to the emerging Web of Data, a cloud of Linked Open Data (LOD) datasets freely available on the Web for access/retrieval. The underlying format is the Resource Description Framework (RDF), a graph-based language. LOD datasets of the cloud, often with an explicit machine readable schema, are inter-linked into a virtually unique global graph which is constantly expanding (32 billion edges in 2011). Data mining within such a huge heterogeneous yet relatively well structured source is both beneficial and challenging: (1) the relational structure of a LOD dataset is hard to process for standard data miners and (2) the available schema knowledge need to be properly exploited to guide the mining process. Our program focuses on pattern mining, i.e., discovery of recurring regularities in data descriptions. The extracted patterns have a wide range of applications from schema refinement to recommendation to decision making in the domain. However, existing pattern miners fail in at least one of the three ways: flatten the graph into a feature vector and hence lose precious information for mining, ignore the schema knowledge or sacrify efficiency for expressiveness. We propose to research on a better trade-off between scalability, expressiveness and coverage of variable pattern languages for RDF datasets. The research will build upon mining methods designed to work with rich schemas (ontologies) albeit with a somewhat different input data and of smaller size. The key challenge is therefore to increase their scope and efficiency. To that end, we shall research on properly recasting them in terms of general data mining problems to enable the application of state-of-the-art data structures, techniques and structural results. The intended applications are analysis of drug adverse reactions (pharmacovigilance), decision making in critical care, design of complex workflows in phylogenetic analysis of genomic data.