Application of fuzzy measures to move towards the cyber-taxonomy

The species inventory of global biodiversity is constantly revised and refined by taxonomic research, through the addition of newly discovered species. This almost three century old project provide a knowledge foundation essential for humankind, and notably to develop appropriate conservation strategies. This task relies on the study of millions of specimens housed all around the world in natural history collections. Since two decades, taxonomy generates a plethoric amount of numeric data every year, and notably through the digitization of collection specimens, gradually transforms into a big data science. In this line, the French National Museum of Natural History (MNHN) has embarked into a major research and engineering challenge within its information system, in order to facilitate the transition towards cyber-taxonomic practices which require a facilitated access to data on reference collection specimens housed all over the world. To this end, a first mandatory step is to automatically complete classification data usually associated to collection specimens found in multiple databases. We use here fuzzy approaches to connect one database with other databases and match identical specimens found in each databases together.