The state of the art in ontology learning: a framework for comparison

Abstract

In recent years there have been some efforts to automate the ontology acquisition and construction process. The proposed systems differ from each other in some factors and have many features in common. This paper presents the state of the art in Ontology Learning (OL) and introduces a framework for classifying and comparing OL systems. The dimensions of the framework concern what to learn, from where to learn it and how it may be learnt. They include features of the input, the methods of learning and knowledge acquisition, the elements learned, the resulting ontology and also the evaluation process. To extract this framework, over 50 OL systems or modules thereof that have been described in recent articles are studied here and seven prominent ones, which illustrate the greatest differences, are selected for analysis according to our framework. In this paper after a brief description of the seven selected systems we describe the dimensions of the framework. Then we place the representative ontology learning systems into our framework. Finally, we describe the differences, strengths and weaknesses of various values for our dimensions in order to present a guideline for researchers to choose the appropriate features to create or use an OL system for their own domain or application.