Unsupervised Knowledge Structuring: Application of Infinite Relational Models to the FCA Visualization

Fumiko Kano Glückstad, Tue Herlau, Mikkel N. Schmidt, Morten Mørup

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    This work presents a conceptual framework for learning an ontological structure of domain knowledge, which combines Jaccard similarity coefficient with the Infinite Relational Model (IRM) by (Kemp et al. 2006) and its extended model, i.e. the normal-Infinite Relational Model (n-IRM) by (Herlau et al. 2012). The proposed approach is applied to a dataset where legal concepts related to the Japanese educational system are defined by the Japanese authorities according to the International Standard Classification of Education (ISCED). Results indicate that the proposed approach effectively structures features for defining groups of concepts in several levels (i.e., concept, category, abstract category levels) from which an ontological structure is systematically visualized as a lattice graph based on the Formal Concept Analysis (FCA) by (Ganter and Wille 1997).
    Original languageEnglish
    Title of host publicationThe 9th International Conference on Signal Image Technology & Internet Based Systems. SITIS 2013
    EditorsKokou Yetongnon, Albert Dipanda , Richard Chbeir
    Place of PublicationLos Alamitos, CA
    Publication date2013
    ISBN (Print)9781479932115
    Publication statusPublished - 2013
    EventThe 9th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS 2013) - Terrsa Conference Center, Kyoto, Japan
    Duration: 2 Dec 20135 Dec 2013
    Conference number: 9


    ConferenceThe 9th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS 2013)
    LocationTerrsa Conference Center
    Internet address

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