AbstractsComputer Science

Learning Terminological Knowledge with High Confidence from Erroneous Data

by Daniel Borchmann

Institution: Technische Universität Dresden
Department: Fakultät Mathematik und Naturwissenschaften
Degree: PhD
Year: 2014
Record ID: 1099199
Full text PDF: http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-152028


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.