Modeling Generalization in Domain Taxonomies Using a Maximum Likelihood Criterion

Zhirayr Hayrapetyan, Susana Nascimento, Trevor Fenner, Dmitry Frolov, Boris Mirkin

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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 involving some errors referred to as “gaps” and “offshoots”. We develop a method to globally maximize the likelihood of a scenario involving gains and losses of the general concept manifested in a fuzzy cluster of leaf nodes of the taxonomy. Probabilities of the gain and loss events are derived from multiple runs of our earlier method of maximum parsimony starting with randomly generated values for the two parameters involved. Supplemented with fuzzy c-means clustering, this allows us to obtain meaningful generalizations for six fuzzy thematic clusters of Data Science topics using over 17000 abstracts from 17 research journals published by Springer.

Original languageEnglish
Title of host publicationInformation Systems and Technologies - WorldCIST 2022
EditorsÁlvaro Rocha, Hojjat Adeli, Gintautas Dzemyda, Fernando Moreira
Place of PublicationCham
PublisherSpringer
Pages141-147
Number of pages7
Volume2
ISBN (Electronic)978-3-031-04819-7
ISBN (Print)978-3-031-04818-0
DOIs
Publication statusPublished - 2022
Event10th World Conference on Information Systems and Technologies, WorldCIST 2022 - Budva, Montenegro
Duration: 12 Apr 202214 Apr 2022

Publication series

NameLecture Notes in Networks and Systems
PublisherSpringer
Volume469
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389

Conference

Conference10th World Conference on Information Systems and Technologies, WorldCIST 2022
Country/TerritoryMontenegro
CityBudva
Period12/04/2214/04/22

Keywords

  • Fuzzy thematic cluster
  • Generalization
  • Maximum likelihood
  • Research tendencies

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