Graph Decomposition Approaches for Terminology Graphs

Mohamed Didi Biha (1) , Bangaly Kaba (2) , Marie-Jean Meurs (3) , Eric Sanjuan (3)

1 LANLG - Laboratoire d'Analyse non linéaire et Géométrie
2 LIMOS - Laboratoire d'Informatique, de Modélisation et d'optimisation des Systèmes
3 LIA - Laboratoire Informatique d'Avignon

We propose a graph-based decomposition methodology of a network of document features represented by a terminology graph. The graph is automatically extracted from raw data based on Natural Language Processing techniques implemented in the TermWatch system. These graphs are Small Worlds. Based on clique minimal separators and the associated graph of atoms: a subgraph without clique separator, we show that the terminology graph can be divided into a central kernel which is a single atom and a periphery made of small atoms. Moreover, the central kernel can be separated based on small optimal minimal separators.

Mots clés: graph algorithms; graph decomposition; polyhedral approach; text mining; topic vizualisation

Domaines: Informatique [cs]

Mohamed Didi Biha, Bangaly Kaba, Marie-Jean Meurs, Eric Sanjuan. Graph Decomposition Approaches for Terminology Graphs. 6th Mexican International Conference on Artificial Intelligence, Nov 2007, Aguascalientes, Mexico. ⟨hal-01318619⟩