  Today with Big Data and data lakes, we are faced of a mass of data that is
very difficult to manage it manually. The protection of personal data in this
context requires an automatic analysis for data discovery. Storing the names of
attributes already analyzed in a knowledge base could optimize this automatic
discovery. To have a better knowledge base, we should not store any attributes
whose name does not make sense. In this article, to check if the name of an
attribute has a meaning, we propose a solution that calculate the distances
between this name and the words in a dictionary. Our studies on the distance
functions like N-Gram, Jaro-Winkler and Levenshtein show limits to set an
acceptance threshold for an attribute in the knowledge base. In order to
overcome these limitations, our solution aims to strengthen the score
calculation by using an exponential function based on the longest sequence. In
addition, a double scan in dictionary is also proposed in order to process the
attributes which have a compound name.
