TOLERATING FUZZINESS IN KEYWORDS BY SIMILARITY SEARCHES

Abstract

One feature of a user‐friendly system is the capability to tolerate fuzziness in the form of names or keywords. The following remarks are a first step towards a model for the intuitive human‐like notion of similarity. This model is characterized by using only the context within single words for a definition of similarity measures. These measures are based on maximal common substrings and abstract syllables. In order to obtain an efficient computation of this formal similarity in large lists, a preselection method is given which uses a simple distance between strings and a precomputed binary relation between character‐pairs and keywords.

Citation

SCHEK, H.J. (1977), "TOLERATING FUZZINESS IN KEYWORDS BY SIMILARITY SEARCHES", Kybernetes, Vol. 6 No. 3, pp. 175-184. https://doi.org/10.1108/eb005450