Learning Description Logic Ontologies: Five Approaches. Where Do They Stand?
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TECHNICAL CONTRIBUTION
Learning Description Logic Ontologies: Five Approaches. Where Do They Stand? Ana Ozaki1,2 Received: 6 March 2020 / Accepted: 25 March 2020 © The Author(s) 2020
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. Keywords Ontology learning · Description logic · Logic and learning
1 Introduction 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]. In this work, 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. The main goal is to find how the symbols of the vocabulary should be related, using the logical constructs available in the ontology language. Desirable goals of an ontology learning process include: 1. the creation of ontologies which are interpretable; expressions should not be overly complex, redundancies should be avoided; 2. the support for learnability of DL expressions formulated in rich ontology languages; 3. efficient algorithms for creating ontologies, requiring a small amount of time and training data; * Ana Ozaki [email protected] 1
Free University of Bozen-Bolzano, Piazza Università, 1, 39100 Bolzano, BZ, Italy
Department of Informatics, University of Bergen, 5020 Bergen, Norway
2
4. limited or no human intervention requirement; 5. the support for learning in unsupervised settings; 6. handling of inconsistencies and noise. Other properties such as explainability and trustability may also be relevant for some approaches. Moreover, once the ontology has been created, it needs to be checked, be maintained, and evolve. This means that other reasoning tasks should also be feasible. Nearly 20 years after the term “ontology learning” was coined by Maedche and Staab [33], it is not a surprise that no approach could accomplish such ambitious and conflicting goals. However, different approaches have addressed some of these goals. 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]
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