STORIES - Semantic Trajectories / Spatio-Temporal trajectories

STORIES is a LIG research team created on January 1, 2025 and dedicated to the study of semantic trajectories.

The STORIES (for Semantic Trajectories / Spatio-Temporal trajectories) team's research aims to design and exploit a digital artifact, called a semantic trajectory, dedicated to representing and analyzing the evolution of an object or phenomenon, or a group of objects or phenomena.

The semantic trajectory is a complex representation of an object's evolution, designed to enable it to be observed and studied.

Once completed, the desired semantic trajectory model needs to be implemented in a physical model. The semantic trajectory data can then be acquired to populate this model. This data can then be processed in a number of ways. Querying, visualizing, comparing, analyzing and interpreting the semantic trajectory data of an object should enable us to better understand the evolution of this object, and to better anticipate or decide on any actions to be taken as a function of this evolution.

The STORIES team designs and implements IT solutions for and with researchers and experts in fields as diverse as the humanities and social sciences (demography, sociology, psychology, economics, history, etc.), literature and linguistics, as well as regional and urban planning, cindynics, medicine and more. This interdisciplinary work is legitimized by the needs expressed by researchers and experts in these fields, and supports their own research in response to many of today's societal and environmental challenges. 

Building on several years of established collaborations, the STORIES team's approach is distinguished both by its recognized expertise in developing semantic trajectories for original study objects (territorial evolution, life courses, etc.), and by the design and operationalization of a complete semantic trajectory processing chain. 

STORIES' work and contributions are mainly in the following fields of research:

• Knowledge representation, the Semantic Web, Knowledge Graphs (KGs) and Linked Open Data, ontological reasoning and semantic enrichment (ontology alignment, entity linking, etc.); 

• Spatial and temporal reasoning;

• Querying (via query languages, or by exploiting the API of an LLM-based generative AI);

• Multi-dimensional data analysis (statistical sequence analysis, unsupervised similarity and classification, supervised classification, pattern mining, sequence prediction, etc.);

• Visualization of spatial, temporal and multidimensional information (datavisualization, geovisualization, etc.);

• Geomatics and geographic information sciences.