ICML-2005 Tutorial

Machine Learning and the Semantic Web

Andreas Hotho, Steffen Staab


Machine Learning and the Semantic Web are two fields of research that allow for rich and intriguing interactions. Firstly, the Semantic Web is an effort to put ontologies as well as explicitly and richly structured semantic data on the WWW for purposes such as query answering, data integration or intelligent reasoning. While some of its core building blocks have achieved a quite sophisticated level, e.g. its representation languages, there is an urgent need to facilitate the creation of ontologies as well as the creation of corresponding data. Machine learning here serves as an important vehicle to achieve this purpose by:
Secondly, such ontologies and semantic data give rise to new possibilities for improving existing machine learning tasks such as
Application of such approaches lie in typical applications like text classification and clustering, but also in improvements and new possibilities for data integration.
Thirdly, the availability of ontologies and semantic data is not only a means to improve existing tasks, it also offers chances for defining new machine learning challenges and applications. The goal of this tutorial is to acquaint the reader with the basics of the Semantic Web and then to introduce him to the different interactions between Semantic Web and Machine Learning that are currently explored. In addition we will try to address efforts which can be provided by the machine learning community to bring up the semantic web. There is a need to support tasks like ontology construction, evolution and mapping or the filling of the knowledge base. Further resulting semantic data should to be analyzed by machine learning algorithm. Both direction can be seen as a challenge for machine learning and can end up in the development of new algorithm. In the tutorial we will try to address this issue and we will show possible direction for a fruitful combination of both areas and future research.

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