Apponto-Pro: An incremental process for ontology learning and population

Ontologies are knowledge representation structures supporting the development of effective and usable software systems. However, the manual construction of ontologies is expensive and error prone. Therefore, this task should be conducted in an automated way. Various techniques and tools for learning the different components of an ontology from textual sources have been developed. These components are classes, hierarchies, non taxonomic relationships, instances, properties and axioms. However, there is a lack of techniques to undertake the learning of all these components together in a common process. This article proposes Apponto-Pro, an incremental process for learning and populating all the elements of an ontology automatically. The process is been evaluated with a case study on the automatic construction of a Family Law application ontology.