How to Support Human Operator in "uncertainty" Managing during the Ontology Learning Process

Abstract

Creating ontologies is an essential while challenging task to be performed by either a human or a system: on one hand it is excessively burdensome for a human operator, on the other it is very complex also for a machine due to the not negligible amount of "uncertainty" that it must be able to manage. In the last years, some attempts have been made to automate this process, but at present, due to the large number of aspects to be covered in the automatic creation of an ontology (such as Domain terminology extraction, Concept discovery, Concept hierarchy derivation, ") satisfactory solutions have not been reached yet. In order to produce efficient tools for both creation and enrichment of ontologies, the participation of the human in such a process still seems necessary. Our approach, that foresees a broader framework for ontology learning, is based by first on the automatic extraction of triples from heterogeneous sources, then on the presentation of the most reliable triples to the human operator for validation purposes. The system provides the user with a series of graphical representations that can give him an overview of the level of uncertainty of the automatically generated ontology. Then provides the user with the possibility to perform SPARQL what-if queries, (i.e. assuming as true the triples filtered according to the level of confidence, the source and the structure of the triples).