Knowledge-Based Systems ( IF 5.921 ) Pub Date : 2020-01-16 , DOI: 10.1016/j.knosys.2020.105508 Silvio Domingos Cardoso; Marcos Da Silveira; Cédric Pruski
With the advances of Artificial Intelligence, the need for annotated data increases. However, the quality of these annotations can be impacted by the evolution of domain knowledge since the relations between successive versions of ontologies are rarely described and the history of concepts is not kept at the ontology level. As a consequence, using datasets annotated at different times becomes a real challenge for data- and knowledge-intensive systems. This work presents a way to address this problem. We introduce a Historical Knowledge Graph (HKG), where information from previous versions of an ontology can be found inside a single graph, reducing storage space (no need for versioning) and data treatment time (no need for laborious analysis of each version of the ontology). The HKG proposed in this work represents the evolutionary aspects of the knowledge in a structural way. Examples of the applicability of an HKG for information retrieval and the maintenance of semantic annotations show the capability of our approach for improving the quality of existing techniques.