Shifting concepts to their associative concepts via bridges

Hongjie Zhai, Makoto Haraguchi, Yoshiaki Okubo, Kiyota Hashimoto, Sachio Hirokawa

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    3 Citations (Scopus)


    This paper presents a pair of formal concept search procedures to find associative connection of concepts via bridge concepts. A bridge is a generalization of a sub-concept of an initial concept. The initial concept is then shifted to other target concepts which are conditionally similar to the initial one within the extent of bridge. A procedure for mining target concepts under the conditional similarity with respect to the bridge is presented based on an object-feature incident relation. Such a bridge concept is constructed in the concept lattice of person-feature incident relation. The latter incident relation is defined by aggregating the former document-feature relation to have more condensed relation, while keeping the variation of possible candidate bridges. Some heuristic rule, named Mediator Heuristics, is furthermore introduced to reflect user's interests and intention. The pair of these two procedures provides an efficient method for shifting initial concepts to target ones via some bridges. We show their usefulness by applying them to Twitter data.

    Original languageEnglish
    Title of host publicationMachine Learning and Data Mining in Pattern Recognition - 9th International Conference, MLDM 2013, Proceedings
    Number of pages15
    Publication statusPublished - 2013
    Event9th International Conference on International Conference on Machine Learning and Data Mining, MLDM 2013 - New York, NY, United States
    Duration: Jul 19 2013Jul 25 2013

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume7988 LNAI
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349


    Other9th International Conference on International Conference on Machine Learning and Data Mining, MLDM 2013
    Country/TerritoryUnited States
    CityNew York, NY

    All Science Journal Classification (ASJC) codes

    • Theoretical Computer Science
    • General Computer Science


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