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Using Taxonomy Tree to Generalize a Fuzzy Thematic Cluster
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Orientador(es)
Resumo(s)
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.
Descrição
D.F. and B.M. acknowledge continuing support by the Academic Fund Program at the National Research University Higher School of Economics (grant 19-04-019 in 2018-2019) and by the International Decision Choice and Analysis Laboratory (DECAN) NRU HSE, in the framework of a subsidy granted to the HSE by the Government of the Russian Federation for the implementation of the the Russian Academic Excellence Project “5-100”. S.N. acknowledges the support by FCT/MCTES, NOVA LINCS (UID/CEC/04516/2019).
Palavras-chave
annotated suffix tree fuzzy cluster gap-offshoot penalty generalization Software Theoretical Computer Science Artificial Intelligence Applied Mathematics
Contexto Educativo
Citação
Editora
Institute of Electrical and Electronics Engineers (IEEE)