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Formalized Conflicts Detection Based on the Analysis of Multiple Emails: An Approach Combining Statistics and Ontologies
Chahnez Zakaria1, Olivier Curé1, Gabriella Salzano1, and Kamel Smaïli2
1Université Paris-Est, IGM Terre Digitale, Marne-la-Vallée, France
chahnez.zakaria@univ-mlv.fr
olivier.cure@univ-mlv.fr
gabriella.salzano@univ-mlv.fr
2Loria, Campus Scientifique, BP 239 54506 Vandoeuvre Lès-Nancy, France
smaili@loria.fr
Abstract. In Computer Supported Cooperative Work (CSCW), it is crucial for project leaders to detect conflicting situations as early as possible. Generally, this task is performed manually by studying a set of documents exchanged between team members. In this paper, we propose a full-fledged automatic solution that identifies documents, subjects and actors involved in relational conflicts. Our approach detects conflicts in emails, probably the most popular type of documents in CSCW, but the methods used can handle other text-based documents. These methods rely on the combination of statistical and ontological operations. The proposed solution is decomposed in several steps: (i) we enrich a simple negative emotion ontology with terms occuring in the corpus of emails, (ii) we categorize each conflicting email according to the concepts of this ontology and (iii) we identify emails, subjects and team members involved in conflicting emails using possibilistic description logic and a set of proposed measures. Each of these steps are evaluated and validated on concrete examples. Moreover, this approach’s framework is generic and can be easily adapted to domains other than conflicts, e.g. security issues, and extended with operations making use of our proposed set of measures.
LNCS 5870, p. 94 ff.
lncs@springer.com
© Springer-Verlag Berlin Heidelberg 2009