Integration Languages for Data-Driven Approaches to Ontology Population and Maintenance

Populating an ontology with a vast amount of data and ensuring the quality of the integration process by means of human supervision seem to be mutually exclusive goals that nevertheless arise as requirements when building practical applications. In our case, we were confronted with the practical problem of populating the EFGT Net, a large-scale ontology that enables thematic reasoning in different NLP applications, out of already existing and partly very large data sources, but on condition of not putting the quality of the resource at risk. We present here our particular solution to this problem, which combines, in a single tool, on one hand an integration language capable of generating new entries for the ontology out of structured data with, on the other hand, a visualization of conflicting generated entries with online ontology editing facilities. This approach appears to enable efficient human supervision of the population process in an interactive way and to be also useful for maintenance tasks.