Management and completion of dynamic Knowledge Graph

In MOKA, we will design and implement methods for managing the evolution of large and dynamic knowledge graphs (KG).

Currently, KG are getting much attention from both industry and academia because they have properties that support the design and

development of more intelligent and intelligible systems by connecting and structuring knowledge, ensuring semantic

interoperability between information systems and providing richer content for automatic reasoning. However, one of the most widely

agreed-upon problems experienced by data scientists and knowledge engineers is the maintenance of such KG over time. Since KG

model the knowledge of a given domain, they have to be frequently updated to reflect the evolution of that domain which demand

significant effort from experts and once updated changes are lost which leads to a general impoverishment of knowledge over time.

To do so, our methods and tools will allow to (i) identify and characterize changes that occurs in KG when the domain evolves which

will significantly reduce the efforts of domain experts in the KG maintenance tasks and (ii) define a mechanism to represent the

evolution of a domain within a so-called Historical Knowledge Graph (HKG) that will allow users having a clear and deep

understanding of the evolution of the domain knowledge over time. The proposed methods and tools will be evaluated on the KG of

the Open Research Knowledge Graph initiative and the data of the space domain. We will have a significant impact on scientific,

technological, economic and societal aspects. This will be made possible thanks to the proposed dissemination, exploitation and

communication plans aimed at a large audience that will facilitate the development of the career of the researcher.