Unsupervised knowledge structuring Application of Infinite Relational Models to the FCA visualization

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

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 publication2013 International Conference on Signal-Image Technology & Internet-Based Systems
PublisherIEEE
Publication date2013
Pages233-240
ISBN (Print)978-1-4799-3211-5
DOIs
Publication statusPublished - 2013
Event9th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS 2013) - Kyoto, Japan
Duration: 2 Dec 20135 Dec 2013
http://www.sitis-conf.org/

Conference

Conference9th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS 2013)
CountryJapan
CityKyoto
Period02/12/201305/12/2013
Internet address

Fingerprint Dive into the research topics of 'Unsupervised knowledge structuring Application of Infinite Relational Models to the FCA visualization'. Together they form a unique fingerprint.

Cite this