Add to ORKGAmong the inferences studied in Description Logics (DLs), induction has been paid increasing attention over the last decade. Indeed, useful non-standard reasoning tasks can be based on the inductive inference. Among them, Concept Learning is about the automated induction of a description for a given concept starting from classified instances of the concept. In this paper we present a formal characterization of Concept Learning in DLs which relies on recent results in Knowledge Representation and Machine Learning
AbstractThis paper introduces a logical model of inductive generalization, and specifically of the m...
This paper introduces a logical model of inductive generalization, and specif-ically of the machine ...
This paper introduces a logical model of inductive generalization, and specifically of the machine l...
Among the inferences studied in Description Logics (DLs), induction has been paid increasing attenti...
Learning in Description Logics (DLs) has been paid increasing attention over the last decade. Severa...
In the field of machine learning different paradigms are used among which inductive learning. A spe...
This paper introduces a formal base in order to talk about theory learning from a Description Logics...
Description Logics (DLs) are a class of knowledge representation formalisms that can represent termi...
The problem of learning logic programs has been researched extensively, but other knowledge represen...
The quest for acquiring a formal representation of the knowledge of a domain of interest has attract...
This paper deals with the problem of learning characteristic concept descriptions from examples and ...
We present a series of theoretical and experimental results on the learnability of description logic...
Abstract With the advent of the Semantic Web, description logics have become one of the most promine...
This paper proposes the use of description logics as a representational framework for learning compo...
Three different formalizations of concept-learning in logic (as well as some variants) are analyzed ...
AbstractThis paper introduces a logical model of inductive generalization, and specifically of the m...
This paper introduces a logical model of inductive generalization, and specif-ically of the machine ...
This paper introduces a logical model of inductive generalization, and specifically of the machine l...
Among the inferences studied in Description Logics (DLs), induction has been paid increasing attenti...
Learning in Description Logics (DLs) has been paid increasing attention over the last decade. Severa...
In the field of machine learning different paradigms are used among which inductive learning. A spe...
This paper introduces a formal base in order to talk about theory learning from a Description Logics...
Description Logics (DLs) are a class of knowledge representation formalisms that can represent termi...
The problem of learning logic programs has been researched extensively, but other knowledge represen...
The quest for acquiring a formal representation of the knowledge of a domain of interest has attract...
This paper deals with the problem of learning characteristic concept descriptions from examples and ...
We present a series of theoretical and experimental results on the learnability of description logic...
Abstract With the advent of the Semantic Web, description logics have become one of the most promine...
This paper proposes the use of description logics as a representational framework for learning compo...
Three different formalizations of concept-learning in logic (as well as some variants) are analyzed ...
AbstractThis paper introduces a logical model of inductive generalization, and specifically of the m...
This paper introduces a logical model of inductive generalization, and specif-ically of the machine ...
This paper introduces a logical model of inductive generalization, and specifically of the machine l...