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

Danmarks Tekniske Universitet

Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › peer review

106 Downloads (Pure)

Abstrakt

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).

Originalsprog	Engelsk
Titel	The 9th International Conference on Signal Image Technology & Internet Based Systems. SITIS 2013
Redaktører	Kokou Yetongnon, Albert Dipanda , Richard Chbeir
Udgivelsessted	Los Alamitos, CA
Forlag	IEEE
Publikationsdato	2013
Sider	233-240
ISBN (Trykt)	9781479932115
Status	Udgivet - 2013
Begivenhed	The 9th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS 2013) - Terrsa Conference Center, Kyoto, Japan Varighed: 2 dec. 2013 → 5 dec. 2013 Konferencens nummer: 9 http://www.sitis-conf.org/

Konference

Konference	The 9th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS 2013)
Nummer	9
Lokation	Terrsa Conference Center
Land/Område	Japan
By	Kyoto
Periode	02/12/2013 → 05/12/2013
Internetadresse	http://www.sitis-conf.org/

Emneord

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

Adgang til dokumentet

Gluckstad_2013Forlagets udgivne version, 2,98 MBLicens: CC BY-NC-ND

Handle.net

Persistent link

Citationsformater

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. red. / Kokou Yetongnon ; Albert Dipanda ; Richard Chbeir. Los Alamitos, CA : IEEE, 2013. s. 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. i K Yetongnon, A Dipanda & R Chbeir (red), The 9th International Conference on Signal Image Technology & Internet Based Systems. SITIS 2013. IEEE, Los Alamitos, CA, s. 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. red. / Kokou Yetongnon; Albert Dipanda ; Richard Chbeir. Los Alamitos, CA : IEEE, 2013. s. 233-240.

Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › 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 -