Sebastien Harispe holds a Master's and PhD in Computer Science from the University of Montpelier II. His research focuses on Artificial Intelligence and more particularly on the diversity of methods which can be used to support decision making from text and knowledge base analysis, e.g. Information and Extraction and Knowledge inference. He proposed several theoretical and practical contributions related to semantic measures. He is the project leader and main developer of the Semantic Measures Library project, a project dedicated to the development of open source software solutions for semantic measures computation and analysis. Sylvie Ranwez is an Associate Professor at the LGI2P Research Center at the School of Mines. Since 2000, she has been interested in the research endeavor of one part of the Artificial Intelligence; Knowledge Engineering. Her research is dedicated to ontologies used as a guideline in conceptual annotation process and information retrieval systems, navigation over numerous resources and visualization. Stefan Janaqi, is a research member of the LGI2P Research Center team at the School of Mines. He holds a PhD in Computer Science from University Joseph Fourier, Grenoble (France), dealing with geometric properties of graphs. His research focuses on mathematical models for optimization, image treatment, evolutionary algorithms and convexity in discrete structures such as graphs. Jacky Montmain received the Master's degree from the Ecole Nationale Superieure d'Ingenieurs Electriciens de Grenoble France in 1987 and a PhD from the National Polytechnic Institute in 1992; both in control theory. He was a research engineer at the French Atomic Energy Commission from 1991 to 2005 where he was appointed as Senior Expert in the field of Mathematics, Computer Sciences, Software, and System Technologies in 2003. He is currently a Professor at the School of Mines. His research interests include the application of artificial intelligence techniques to model-based diagnosis and supervision, industrial performance improvement, multicriteria and fuzzy approaches to decision-making.
“...this book does make a solid reference work on knowledge-based similarity measures, and it provides good overview of evaluation protocols that are currently out there.” – Emiel van Miltenburg, Vrije Universiteit Amsterdam for Linguist List
“Harispe et al. offer a coherent and most-welcome unified view of the vast literature on semantic similarity, covering both corpus-based and ontological methods. It fills a gap in the current literature and is rich with references and interesting insights into the state of the art, from the theory to the experimental evaluations. Researchers and students will find it a great resource to quickly get started in the area.” – Denilson Barbosa for ACM Computing Reviews