Learning Description Logic Axioms from Discrete Probability Distributions over Description Graphs
Research output: Contribution to book/conference proceedings/anthology/report › Conference contribution › Contributed › peer-review
Contributors
Abstract
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.
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.
Details
Original language | English |
---|---|
Title of host publication | Logics in Artificial Intelligence |
Editors | Francesco Calimeri, Nicola Leone, Marco Manna |
Publisher | Springer, Berlin [u. a.] |
Pages | 399-417 |
Number of pages | 19 |
Publication status | Published - 7 May 2019 |
Peer-reviewed | Yes |
Publication series
Series | Lecture Notes in Computer Science, Volume 11468 |
---|---|
ISSN | 0302-9743 |
External IDs
Scopus | 85065957678 |
---|---|
ORCID | /0000-0003-0219-0330/work/153109377 |