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Regular version of the site

Book chapter

Using Taxonomy Tree to Generalize a Fuzzy Thematic Cluster

P. 1-6.
Frolov D., Mirkin B., Nascimento S., Fenner T.

This paper presents an algorithm, ParGenFS, for generalizing, or “lifting”, a fuzzy set of topics to higher ranks of a hierarchical taxonomy of a research domain. The algorithm ParGenFS finds a globally optimal generalization of the topic set to minimize a penalty function, by balancing the number of introduced “head subjects” and related errors, the “gaps” and “offshoots”, differently weighted. This leads to a generalization of the topic set in the taxonomy. The usefulness of the method is illustrated on a set of 17685 abstracts of research papers on Data Science published in Springer journals for the past 20 years. We extracted a taxonomy of Data Science from the international Association for Computing Machinery Computing Classification System 2012 (ACM-CCS). We find fuzzy clusters of leaf topics over the text collection, lift them in the taxonomy, and interpret found head subjects to comment on the tendencies of current research.