- Authors
- Felix Distel
- title
- Learning Description Logic Knowledge Bases from Data Using Methods from Formal Concept Analysis
- Please use the following URL when quoting:
- https://nbn-resolving.org/urn:nbn:de:bsz:14-qucosa-70199
- Date of submission
- 21.12.2010
- Date of defense
- 27.04.2011
- Abstract (EN)
- Description Logics (DLs) are a class of knowledge representation formalisms that can represent terminological and assertional knowledge using a well-defined semantics. Often, knowledge engineers are experts in their own fields, but not in logics, and require assistance in the process of ontology design. This thesis presents three methods that can extract terminological knowledge from existing data and thereby assist in the design process. They are based on similar formalisms from Formal Concept Analysis (FCA), in particular the Next-Closure Algorithm and Attribute-Exploration. The first of the three methods computes terminological knowledge from the data, without any expert interaction. The two other methods use expert interaction where a human expert can confirm each terminological axiom or refute it by providing a counterexample. These two methods differ only in the way counterexamples are provided.
- Keywords (DE)
- Beschreibungslogik, Formale Begriffsanalyse, Wissensrepräsentation
- Keywords (EN)
- Description Logics, Formal Concept Analysis, Knowledge Representation
- Classification (DDC)
- 001, 131
- Classification (RVK)
- ST 125
- Standardizd keywords (GND)
- Logik, Ontologie <Wissensverarbeitung>
- Examiner
- Prof. Dr. rer. nat. Gerd Stumme
- Supervisor
- Prof. Dr.-Ing. Franz Baader
- Awarding institution
- Technische Universtiät Dresden, Dresden
- URN Qucosa
- urn:nbn:de:bsz:14-qucosa-70199
- Qucosa date of publication
- 29.06.2011
- Document type
- doctoral_thesis
- Document language
- English
- licence