tud Learning Terminological Knowledge with High Confidence from Erroneous Data 2014-09-17 [Electronic ed.] 4519974-7 Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden prv Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, Dresden Fakultät Mathematik und Naturwissenschaften Professur für Algebraische Strukturtheorie male Königs Wusterhausen Description logics knowledge bases are a popular approach to represent terminological and assertional knowledge suitable for computers to work with. Despite that, the practicality of description logics is impaired by the difficulties one has to overcome to construct such knowledge bases. Previous work has addressed this issue by providing methods to learn valid terminological knowledge from data, making use of ideas from formal concept analysis. A basic assumption here is that the data is free of errors, an assumption that can in general not be made for practical applications. This thesis presents extensions of these results that allow to handle errors in the data. For this, knowledge that is "almost valid" in the data is retrieved, where the notion of "almost valid" is formalized using the notion of confidence from data mining. This thesis presents two algorithms which achieve this retrieval. The first algorithm just extracts all almost valid knowledge from the data, while the second algorithm utilizes expert interaction to distinguish errors from rare but valid counterexamples. 510 SK 150 Formale Begriffsanalyse, Beschreibungslogiken, Wissensextraktion Formal Concept Analysis, Description Logics, Learning urn:nbn:de:bsz:14-qucosa-152028 415331935 Technische Universität Dresden dgg Technische Universität Dresden, Dresden Daniel Borchmann 1984-11-23 aut Bernhard Ganter Prof. Dr. dgs rev Franz Baader Prof. Dr. dgs Sergei Kuznetsov Prof. Dr. rev eng 2014-05-23 2014-09-09 born digital Daniel Borchmann daniel.borchmann@mailbox.tu-dresden.de doctoral_thesis