- Titel
- Learning description logic axioms from discrete probability distributions over description graphs
- Untertitel
- Extended Version
- Zitierfähige Url:
- https://nbn-resolving.org/urn:nbn:de:bsz:14-qucosa2-796087
- Schriftenreihe
- LTCS-Report
- Bandnummer
- 18-12
- Erstveröffentlichung
- 2018
- DOI
- https://doi.org/10.25368/2022.247
- Abstract (EN)
- Description logics in their standard setting only allow for representing and reasoning with crisp knowledge without any degree of uncertainty. Of course, this is a serious shortcoming for use cases where it is impossible to perfectly determine the truth of a statement. For resolving this expressivity restriction, probabilistic variants of description logics have been introduced. Their model-theoretic semantics is built upon so-called probabilistic interpretations, that is, families of directed graphs the vertices and edges of which are labeled and for which there exists a probability measure on this graph family. Results of scientific experiments, e.g., in medicine, psychology, or biology, that are repeated several times can induce probabilistic interpretations in a natural way. In this document, we shall develop a suitable axiomatization technique for deducing terminological knowledge from the assertional data given in such probabilistic interpretations. More specifically, we consider a probabilistic variant of the description logic EL⊥, and provide a method for constructing a set of rules, so-called concept inclusions, from probabilistic interpretations in a sound and complete manner.
- Freie Schlagwörter (DE)
- Data Mining, Wissenserwerb, wahrscheinlichkeitstheoretische Beschreibungslogik, Wissensdatenbank, Wahrscheinlichkeitsauffassung
- Freie Schlagwörter (EN)
- data mining, knowledge acquisition, probabilistic description logic, knowledge base, probabilistic interpretation
- Klassifikation (DDC)
- 004
- Klassifikation (RVK)
- ST 136
- Publizierende Institution
- Technische Universität Dresden, Dresden
- Version / Begutachtungsstatus
- angenommene Version / Postprint / Autorenversion
- URN Qucosa
- urn:nbn:de:bsz:14-qucosa2-796087
- Veröffentlichungsdatum Qucosa
- 20.06.2022
- Dokumenttyp
- Bericht
- Sprache des Dokumentes
- Englisch
- Lizenz / Rechtehinweis
CC BY 4.0