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

Publikation: Forskning - peer reviewKonferencebidrag i proceedings

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).

Publikationsoplysninger

OriginalsprogEngelsk
TitelThe 9th International Conference on Signal Image Technology & Internet Based Systems. SITIS 2013
RedaktørerKokou Yetongnon, Albert Dipanda , Richard Chbeir
Udgivelses stedLos Alamitos, CA
UdgiverIEEE
Publikationsdato2013
Sider233-240
ISBN (trykt)9781479932115
StatusUdgivet - 2013
Scopus citationer
BegivenhedThe 9th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS 2013) - Kyoto, Japan

Konference

KonferenceThe 9th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS 2013)
Nummer9
LokationTerrsa Conference Center
LandJapan
ByKyoto
Periode02/12/201305/12/2013
Internetadresse

    Emneord

  • Ontology learning, Knowledge structuring, Semantic representation, Unsupervised machine learning, Infinite Relational Model, Formal Concept Analysis

ID: 39060923