Unsupervised Knowledge Structuring

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Unsupervised Knowledge Structuring

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Title: Unsupervised Knowledge Structuring
Application of Infinite Relational Models to the FCA Visualization
Author: Glückstad, Fumiko Kano; Herlau, Tue; Schmidt, Mikkel N.; Mørup, Morten
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).
URI: http://hdl.handle.net/10398/8913
Date: 2014-04-23
Notes: Post print of Signal-Image Technology & Internet-Based Systems (SITIS), 2013 International Conference on Signal Image Technology & Internet Based Systems, Page(s): 233-240

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