Graph decomposition approaches for terminology graphs

Mohamed Didi Biha, Bangaly Kaba, Marie Jean Meurs, Eric Sanjuan

Research output: Chapter in Book/Report/Conference proceedingPublished Conference contributionpeer-review

7 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationMICAI 2007
Subtitle of host publicationAdvances in Artificial Intelligence - 6th Mexican International Conference on Artificial Intelligence, Proceedings
Number of pages11
Publication statusPublished - 2007
Event6th Mexican International Conference on Artificial Intelligence, MICAI 2007 - Aguascalientes, Mexico
Duration: 4 Nov. 200710 Nov. 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4827 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference6th Mexican International Conference on Artificial Intelligence, MICAI 2007


  • Graph algorithms
  • Graph decomposition
  • Polyhedral approach
  • Text mining
  • Topic vizualisation


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