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