Mario Lezoche

Dmitry Morozov, Mario Lezoche, Hervé Panetto

Formal Concept Analysis (FCA) is a well-established method for data analysis which finds many applications in data mining. Its extension on complex data representation formats brought a wave of new applications to the problems such as gene expression mining, prediction of toxicity of chemical compounds or clustering of sequences in process event logs. Insipired from this work our research inherits their model and designs an experiment for mining electroencephalographic recordings for patterns of sleep spindles. The contribution of this paper lies in the specification of desritizition procedure and the architecture of FCA experiment. We also provide some reflection on the related research papers.

Y. Liao, M. Lezoche, H. Panetto et al.

Nowadays, the need for system interoperability in or across enterprises has become more and more ubiquitous. Lots of research works have been carried out in the information exchange, transformation, discovery and reuse. One of the main challenges in these researches is to overcome the semantic heterogeneity between enterprise applications along the lifecycle of a product. As a possible solution to assist the semantic interoperability, semantic annotation has gained more and more attentions and is widely used in different domains. In this paper, based on the investigation of the context and the related works, we identify some existing drawbacks and propose a formal semantic annotation approach to support the semantics enrichment of models in a PLM environment.