[en] Since the development of ontologies from scratch requires much time and many resources, the activity of knowledge acquisition constitutes one of the most impor- tant steps at the beginning of the ontology development process. This activity is essential in all the different methodologies for ontology design as a previous step to the conceptualization and formalization phases. And as its name indicates, this activity is devoted to gather all available knowledge resources describing the domain of the ontology and identify the most important terms in the domain.
This chapter is focused on the study of methods and techniques for the (semi-) automatic processing of knowledge resources that may alleviate the work of knowledge acquisition. This task is known as ontology learning in the literature of ontological engineering. The aim of ontology learning is to apply the most appropriate methods to transform unstructured (e.g., text corpora), semi-structured (e.g., folksonomies, HTML pages) and structured data sources (e.g., databases, thesauri) into conceptual structures. The methods of ontology learning are usually connected with the activity of ontology population, which also relies on (semi-)automatic methods to transform unstruc- tured, semi-structured and structured data sources into instance data (i.e., instances of ontology concepts).
LEMA-Local Environment Management and Analysis ; Lepur : Centre de Recherche en Sciences de la Ville, du Territoire et du Milieu rural
Fondation Européenne de la Science = European Science Foundation - ESF