Abstract
The quest for acquiring a formal representation of the knowledge of a domain of interest has attracted researchers with various backgrounds into a diverse field called ontology learning. We highlight classical machine learning and data mining approaches that have been proposed for (semi-)automating the creation of description logic (DL) ontologies. These are based on association rule mining, formal concept analysis, inductive logic programming, computational learning theory, and neural networks. We provide an overview of each approach and how it has been adapted for dealing with DL ontologies. Finally, we discuss the benefits and limitations of each of them for learning DL ontologies.
Highlights
The quest for acquiring a formal representation of the knowledge of a domain of interest has attracted researchers with various backgrounds and both practical and theoretical inquires into a diverse field called ontology learning [30, 33]
We focus on approaches for building description logic (DL) ontologies assuming that the vocabulary and the language of the ontology to be created are known
These are based on association rule mining (ARM) [1], formal concept analysis (FCA) [19], inductive logic programming (ILP) [35], computational learning theory (CLT) [44], and neural networks (NNs) [34]
Summary
The quest for acquiring a formal representation of the knowledge of a domain of interest has attracted researchers with various backgrounds and both practical and theoretical inquires into a diverse field called ontology learning [30, 33]. We highlight five approaches coming from machine learning and data mining which have been proposed for (semi-)automating the creation of DL ontologies. These are based on association rule mining (ARM) [1], formal concept analysis (FCA) [19], inductive logic programming (ILP) [35], computational learning theory (CLT) [44], and neural networks (NNs) [34]. The adaptations of the approaches to the problem of learning DL ontologies often come with the same benefits and limitations as the original approach To show this effect, for each of the five approaches, we start by presenting the original proposal and explain how it has been adapted for dealing with DL ontologies.
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