Generation of Ontologies from Linked Data

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In 2001, Tim Berners-Lee introduced the term Semantic Web in order to refer to what can be perceived as the future of the internet: a web of machine-interpretable content that can be processed by automatic agents in a meaningful way. Since achieving this ambitious goal requires both an explication and formalization of relevant domain knowledge, ontology languages such as RDFS and OWL have emerged as a means for unambiguous knowledge specification. However, the realization of the semantic web as envisioned by Tim Berners-Lee and even more the wide-spread use of intelligent, reasoning-based applications is still hampered by the lack of ontological resources. The vast amount of linked data in the form of RDF triples which is out there on the Web can be considered an important step forward on the way to the semantic web, but it lacks the necessary degree of expressivity as well as the semantic and syntactic accuracy which is an indispensable requirement for logical inference that yields non-obvious conclusions. In this project, we will develop new methods for the acquisition of structured knowledge representations which leverage existing contents on the Web of data. By applying logical and statistical relational learning to the vast amounts of linked data while reusing manually engineered formal ontologies whenever possible we hope to provide means for bootstrapping the realization of the semantic web. A significant contribution of the project will be the development of novel, integrated learning approaches, which can handle the particular requirements of Linked Open Data in terms of scalability and robustness. Experiments will demonstrate the efficiency and effectiveness of our methods when it comes to different domains and application scenarios the most central one being the task of automatic ontology engineering support and debugging of knowledge bases.