Application of Reasoning on Complex and Evolving Data: Methods and Use-Cases

During the last decade, the international community has been interested in the processes of publication and refinement of Knowledge Graphs (KGs) through initiatives such as the Web of Data. A multitude of methods and systems have been developed to address issues related to the acquisition, publication and exploitation of KGs. In particular, considerable progress has been made in the construction and the enrichment of KGs: ontology matching, data integration, fact prediction and validation. This happened thanks to the use of techniques developed in the fields of knowledge representation, reasoning and machine learning.  With these advances, more and more industrial applications are now able to produce and process KGs. However, the produced KGs are complex and evolving, which open several new issues.

In this workshop, we seek contributions describing methods and uses-cases that rely on the application of reasoning and machine learning on complex, uncertain and evolving knowledge graphs. These contributions can be applied to different domains such as smart cities, smart health, smart farming, digital humanities and automation of business processes.