Offre en lien avec l’Action/le Réseau : – — –/– — –
Laboratoire/Entreprise : ICube Strasbourg
Durée : 3 ans
Contact : franco.giustozzi@insa-strasbourg.fr
Date limite de publication : 2026-05-11
Contexte :
Environmental restoration projects generate large volumes of heterogeneous documentation, including technical reports, project plans, cartographic materials, engineering drawings, and photographic records. These materials contain valuable but fragmented knowledge describing intervention strategies, environmental contexts, technical constraints, and outcomes.
Within the TETRA project (ANR-22-FAI2-0006), previous research efforts primarily concentrated on text-based knowledge extraction using Large Language Models (LLMs), enabling the structuring of restoration knowledge from technical and narrative reports. While this approach demonstrated the potential of large language models for semantic modeling and ontology enrichment, it remained largely confined to textual sources. However, restoration documentation increasingly includes rich visual materials, such as maps, technical drawings, aerial imagery, and photographic records that contain complementary and sometimes critical information not explicitly described in text. This PhD builds upon the foundations established in TETRA by extending the extraction paradigm toward a unified multimodal framework. The central hypothesis is that integrating textual and visual understanding through advanced Vision-Language Models (VLMs) can substantially improve the completeness, semantic consistency, and interpretability of structured environmental knowledge graphs.
Sujet :
The FUSION-KG PhD aims to design a unified multimodal semantic extraction framework capable of transforming heterogeneous environmental documentation into structured, interpretable, and queryable knowledge graphs. The ambition is not only to extract information from text and images, but to develop a coherent framework in which multimodal understanding and structured external knowledge
jointly contribute to reliable and semantically consistent knowledge graph construction.
The work involves the systematic modeling and characterization of heterogeneous documentary sources, including technical reports, maps, engineering drawings, aerial and satellite imagery, and photographic
records of restoration interventions. These materials provide complementary yet often fragmented accounts of intervention types, spatial configurations, temporal phases, environmental parameters, constraints, and outcomes. A major challenge lies in ensuring that information extracted from visual and textual modalities is semantically aligned and represented within a shared conceptual framework.
Profil du candidat :
The doctoral contract is awarded by the doctoral school’s selection committee through a competitive process in which the candidates’ merit is a key factor
Formation et compétences requises :
Education: Student about to graduate a Master or Engineer (Bac + 5) with a specialization in Computer Science.
Specific knowledge: Knowledge on data science methods, knowledge representation and reasoning, knowledge graphs.
Languages: Python, java, owl/sparql.
Ability to work with experts who are not computer scientists. Interest in the application domain would be appreciated.
Adresse d’emploi :
ICube laboratory (CNRS UMR 7357),
300 boulevard Sebastien Brant
BP 10413
67412 ILLKIRCH cedex
Document attaché : 202603151916_Sujet_These_ED_VLM.pdf