Intro‎ > ‎

Goals

Large amounts of data increasingly becoming available and described using real-life ontologies represented in Semantic Web languages, recently opened up the possibility for interesting real-world data mining applications on the Semantic Web. However, exploiting this global resource of data requires new kinds of approaches for data mining and data analysis that would be able to deal at the same time with its scale and with the complexity, expressiveness, and heterogeneity of the representation languages, leverage on availability of ontologies and explicit semantics of the resources, and account for novel assumptions (e.g., open world) that underlie reasoning services within the Semantic Web.

The workshop will try to address the issues above, focusing in particular on the problems of how machine learning techniques, such as statistical learning methods and inductive forms of reasoning, can work directly on the richly structured Semantic Web data and exploit the Semantic Web technologies, what is the value added of machine learning methods for the Semantic Web, and what are the challenges for developers of machine learning techniques for the Semantic Web data, for example in the area of ontology mining.