The goal is about thinking how to capitalize on the amount of knowledge developed during the last decade and use it in a multidisciplinary context for understanding city evolution and its capacity to become more sustainable and resilient. This proposal is made possible thanks to a strong collaboration between LIRIS Laboratory and Metropole of Lyon. This project is fully integrated within the LIRIS laboratory where it brings the necessary transversal approach to talk about smart cities between data science specialists (BD team) and graphic computing specialists (Origami team) since it is a question of keeping the strong link between vector data and associated semantics. The LIRIS is also a key partner when it comes to working in the field of Artificial Intelligence (AI).

As an element of context, research is not just about publications and providing code. Working in an urban context implies rethinking and discovering the unexplored part of knowledge (data, code, literature, and process) like exploring the submerged part of an iceberg. The knowledge about the city evolves, and each new study can lead to revising assumptions or completing the knowledge about some objects that constitute the city. For example, a simple photo exhumed from a newly found archive can narrow the range of a building's existence by demonstrating that it was built later than previously known. The evolution of knowledge also implies considering the evolution of the meaning of the vocabulary, in the representation of knowledge about the city. For example, a building that met accessibility standards for disabled people in the 1980s meets certain criteria that may not be sufficient in 2022, as the rules have evolved. The use of this type of information may therefore be different depending on when the building was declared compliant, but also on when this information appeared in the knowledge base. Moreover, the knowledge linked to the data itself can be used to intelligently publish this data. For example, when publishing data from the Lyon metropolitan area, the question arises whether published derived data will be identical to previously published data or whether a new version should be published. This raises the question of the reproducibility of a calculation, reproducibility linked not only to the initial data but also to the codes used to calculate the derived data.

In this thesis, several challenges are identified:

• Management of temporal graph data on knowledge (related to code, data, project, workflow)
• Ability to generate new information about derived knowledge; how to manage it in a context of knowledge versioning needs, in particular in a context of reproducibility of experiments.
• To propose new concepts of organization and interrogation of knowledge, allowing it to scale.
• Ability to propose a prototype based on the LIRIS Pagoda and UD-SV platforms and to demonstrate the feasibility of the proposed approaches.
• Ability to propose the necessary tools for use by non-data scientists.