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Today with Big Data and data lakes, we are faced of a mass of data that isvery difficult to manage it manually. The protection of personal data in thiscontext requires an automatic analysis for data discovery. Storing the names ofattributes already analyzed in a knowledge base could optimize this automaticdiscovery. To have a better knowledge base, we should not store any attributeswhose name does not make sense. In this article, to check if the name of anattribute has a meaning, we propose a solution that calculate the distancesbetween this name and the words in a dictionary. Our studies on the distancefunctions like N-Gram, Jaro-Winkler and Levenshtein show limits to set anacceptance threshold for an attribute in the knowledge base. In order toovercome these limitations, our solution aims to strengthen the scorecalculation by using an exponential function based on the longest sequence. Inaddition, a double scan in dictionary is also proposed in order to process theattributes which have a compound name.

"hasSignification()": une nouvelle fonction de distance pour soutenir la d\'etection de donn\'ees personnelles