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

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

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

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

Technical University of Denmark

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

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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 language	English
Title of host publication	The 9th International Conference on Signal Image Technology & Internet Based Systems. SITIS 2013
Editors	Kokou Yetongnon, Albert Dipanda , Richard Chbeir
Place of Publication	Los Alamitos, CA
Publisher	IEEE
Publication date	2013
Pages	233-240
ISBN (Print)	9781479932115
Publication status	Published - 2013
Event	The 9th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS 2013) - Terrsa Conference Center, Kyoto, Japan Duration: 2 Dec 2013 → 5 Dec 2013 Conference number: 9 http://www.sitis-conf.org/

Conference

Conference	The 9th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS 2013)
Number	9
Location	Terrsa Conference Center
Country/Territory	Japan
City	Kyoto
Period	02/12/2013 → 05/12/2013
Internet address	http://www.sitis-conf.org/

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Kano Glückstad, Fumiko ; Herlau, Tue ; Schmidt, Mikkel N. et al. / Unsupervised Knowledge Structuring : Application of Infinite Relational Models to the FCA Visualization. The 9th International Conference on Signal Image Technology & Internet Based Systems. SITIS 2013. editor / Kokou Yetongnon ; Albert Dipanda ; Richard Chbeir. Los Alamitos, CA : IEEE, 2013. pp. 233-240

@inproceedings{4bbe0d577396461b83bf0bb9fa8803ef,

title = "Unsupervised Knowledge Structuring: Application of Infinite Relational Models to the FCA Visualization",

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).",

keywords = "Ontology learning, Knowledge structuring, Semantic representation, Unsupervised machine learning, Infinite Relational Model, Formal Concept Analysis",

author = "{Kano Gl{\"u}ckstad}, Fumiko and Tue Herlau and Schmidt, {Mikkel N.} and Morten M{\o}rup",

year = "2013",

language = "English",

isbn = "9781479932115",

pages = "233--240",

editor = "Yetongnon, {Kokou } and {Dipanda }, {Albert } and Chbeir, {Richard }",

booktitle = "The 9th International Conference on Signal Image Technology & Internet Based Systems. SITIS 2013",

publisher = "IEEE",

address = "United States",

note = "null ; Conference date: 02-12-2013 Through 05-12-2013",

url = "http://www.sitis-conf.org/ ",

}

Kano Glückstad, F, Herlau, T, Schmidt, MN & Mørup, M 2013, Unsupervised Knowledge Structuring: Application of Infinite Relational Models to the FCA Visualization. in K Yetongnon, A Dipanda & R Chbeir (eds), The 9th International Conference on Signal Image Technology & Internet Based Systems. SITIS 2013. IEEE, Los Alamitos, CA, pp. 233-240, The 9th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS 2013), Kyoto, Japan, 02/12/2013.

Unsupervised Knowledge Structuring : Application of Infinite Relational Models to the FCA Visualization. / Kano Glückstad, Fumiko ; Herlau, Tue; Schmidt, Mikkel N. et al.

The 9th International Conference on Signal Image Technology & Internet Based Systems. SITIS 2013. ed. / Kokou Yetongnon; Albert Dipanda ; Richard Chbeir. Los Alamitos, CA : IEEE, 2013. p. 233-240.

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

TY - GEN

T1 - Unsupervised Knowledge Structuring

AU - Kano Glückstad, Fumiko

AU - Herlau, Tue

AU - Schmidt, Mikkel N.

AU - Mørup, Morten

N1 - Conference code: 9

PY - 2013

Y1 - 2013

N2 - 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).

AB - 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).

KW - Ontology learning

KW - Knowledge structuring

KW - Semantic representation

KW - Unsupervised machine learning

KW - Infinite Relational Model

KW - Formal Concept Analysis

M3 - Article in proceedings

SN - 9781479932115

SP - 233

EP - 240

BT - The 9th International Conference on Signal Image Technology & Internet Based Systems. SITIS 2013

A2 - Yetongnon, Kokou

A2 - Dipanda , Albert

A2 - Chbeir, Richard

PB - IEEE

CY - Los Alamitos, CA

Y2 - 2 December 2013 through 5 December 2013

ER -