Clustering of Conceptual Graphs with Sparse Data

This paper gives a theoretical framework for clustering a set of conceptual graphs characterized by sparse descriptions. The formed clusters are named in an intelligible manner through the concept of stereotype, based on the notion of default generalization. The cognitive model we propose relies on sets of stereotypes and makes it possible to save data in a structured memory.

• Type: Conference
• Year: 2004
• Where: ICCS
• Authors: Jean-Gabriel Ganascia, Julien Velcin
• Keywords: Conceptual Graphs, Default Generalization, ICCS 2004, Intelligible Manner