Computational Generalization in Taxonomies Applied to: (1) Analyze Tendencies of Research and (2) Extend User Audiences
Oct 2019
We define a most specific generalization of a fuzzy set of topics assigned to leaves of the rooted tree of a
domain taxonomy. This generalization lifts the set to its “head subject” node in the higher ranks of the
taxonomy tree. The head subject is supposed to “tightly” cover the query set, possibly bringing in some
errors referred to as “gaps” and “offshoots”. Our method, ParGenFS, globally minimizes a penalty function
combining the numbers of head subjects and gaps and offshoots, differently weighted. Two applications
are considered: (1) analysis of tendencies of research in Data Science; (2) audience extending for
programmatic targeted advertising online. The former involves a taxonomy of Data Science derived from
the celebrated ACM Computing Classification System 2012. Based on a collection of research papers
published by Springer 1998–2017, and applying in-house methods for text analysis and fuzzy clustering,
we derive fuzzy clusters of leaf topics in learning, retrieval and clustering. The head subjects of these
clusters inform us of some general tendencies of the research. The latter involves publicly available IAB
Tech Lab Content Taxonomy. Each of about 25 mln users is assigned with a fuzzy profile within this
taxonomy, which is generalized offline using ParGenFS. Our experiments show that these head subjects
effectively extend the size of targeted audiences at least twice without loosing quality.
Springer Nature
Dmitry Frolov,
Susana Nascimento, Trevor Fenner,
Zina Taran,
Boris Mirkin
