Presentation

Associate Professor in Computer science at Grenoble INP, Univ. Grenoble Alpes

Since 2020, September:

  • Teacher at Grenoble IAE: Management des Systèmes d’Information et des Flux department.
  • Researcher at Laboratoire d’Informatique de Grenoble (LIG): in the Stories group Semantic Trajectories

Research interests: Geographic Information System, Open Data, Knowledge Graph, Machine Learning, Spatiotemporal data, Modeling Evolution, Interoperability.

Research project:
My research project focuses on representing the evolution of data over time within the Web of distributed data—also known as the Linked Open Data (LOD) Cloud or Semantic Web. In many domains, access to only the most recent versions of a dataset, or to multiple disconnected versions, limits users’ ability to understand the data and how it has evolved.
For instance, if a statistical indicator measuring a region’s population shows a sharp increase between time t and time t+1, automatically describing this change (e.g., tagging it with a semantic label like “strong increase”) and contextualizing it—by linking to encyclopedic knowledge bases or social media data—enhances comprehension. It helps generate new knowledge about the data’s evolution and the possible reasons behind it. When this contextualized knowledge is structured within a network and published on the LOD Cloud, it becomes reusable by software tools. The objective is to produce standardized, interoperable Spatio-Temporal Knowledge Graphs (ST-KGs) that comply with FAIR principles, ensuring their accessibility and reusability, particularly by digital twins or predictive artificial intelligence programs.
However, representing change over time presents both conceptual and technical challenges that must be addressed to meet the growing demand for data openness among public organizations. My project aims to develop ontological models and software solutions for the publication and integration of evolving datasets in the LOD Cloud.
Ultimately, the goal is to build and automatically populate ST-KGs that bring together geographic, historical, and statistical data within the LOD Cloud. These KGs support stakeholders and citizens in understanding spatial dynamics over time. This project lies at the intersection of Semantic Web technologies and Geographic Information Systems (GIS).