Wimmics: web-instrumented human-machine interactions, communities and semantics
Wimmics stands for Web-Instrumented huMan-Machine Interactions, Communities, and Semantics.
Our challenge is to bridge formal semantics and social semantics on the web. We are a joint research team between Inria Centre at UniversitÃĐ CÃīte d'Azur and I3S (CNRS and UniversitÃĐ CÃīte d'Azur). Our research areas are graph-oriented knowledge representation, reasoning and operationalization to model and support actors, actions and interactions in web-based epistemic communities. The application of our research is supporting and fostering interactions in online communities and management of their resources. Wimmics is a follow-up of the teams Acacia, Edelweiss and Kewi.
Inria field: Perception, Cognition, Interaction
Inria theme: Knowledge and Data Representation and Management
CNRS institute: Sciences Informatiques
I3S division: SPARKS: Scalable and Pervasive softwARe and Knowledge Systems
Our challenge is to bridge formal semantics and social semantics on the web. We are a joint research team between Inria Centre at UniversitÃĐ CÃīte d'Azur and I3S (CNRS and UniversitÃĐ CÃīte d'Azur). Our research areas are graph-oriented knowledge representation, reasoning and operationalization to model and support actors, actions and interactions in web-based epistemic communities. The application of our research is supporting and fostering interactions in online communities and management of their resources. Wimmics is a follow-up of the teams Acacia, Edelweiss and Kewi.
Inria field: Perception, Cognition, Interaction
Inria theme: Knowledge and Data Representation and Management
CNRS institute: Sciences Informatiques
I3S division: SPARKS: Scalable and Pervasive softwARe and Knowledge Systems
Best paper at Workshop RAGE-KG 2025
Best paper at Workshop RAGE-KG 2025 for the paper “User Interface and Agent Interface for Online Generation of Knowledge Graphâs Competency Questions and Question-Query Training Sets“, Yousouf Taghzouti, Franck Michel, Tao Jiang, Louis-Felix Nothias and Fabien Gandon. Few question-query datasets exist for fine-tuning large language models on tasks such asâĶ
PhD defence: CÃĐlian Ringwald
PhD defence: “Learning RDF pattern extractors for a language from dual bases Wikipedia/LOD” by CÃĐlian Ringwald the 19th of December Abstract: Whether automatically extracted from structured elements of articles or centrally populated and crowdsourced, the linked open data published by DBpedia and Wikidata now offer rich and complementary views ofâĶ
