Unsupervised Knowledge Structuring

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Unsupervised Knowledge Structuring

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dc.contributor.author Glückstad, Fumiko Kano
dc.contributor.author Herlau, Tue
dc.contributor.author Schmidt, Mikkel N.
dc.contributor.author Mørup, Morten
dc.date.accessioned 2014-04-23T06:52:48Z
dc.date.available 2014-04-23T06:52:48Z
dc.date.issued 2014-04-23
dc.identifier.uri http://hdl.handle.net/10398/8913
dc.description.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). en_US
dc.format.extent 8 en_US
dc.language eng en_US
dc.subject.other Ontology learning en_US
dc.subject.other Knowledge structuring en_US
dc.subject.other Semantic representation en_US
dc.subject.other Unsupervised machine learning en_US
dc.subject.other Infinite Relational Model en_US
dc.subject.other Formal Concept Analysis en_US
dc.title Unsupervised Knowledge Structuring en_US
dc.type cp en_US
dc.contributor.corporation Copenhagen Business School. CBS en_US
dc.contributor.department Department of International Business Communication en_US
dc.contributor.departmentshort IBC en_US
dc.contributor.departmentuk Department of International Business Communication en_US
dc.contributor.departmentukshort IBC en_US
dc.description.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 en_US
dc.publisher.city Frederiksberg en_US
dc.publisher.year 2013 en_US
dc.title.subtitle Application of Infinite Relational Models to the FCA Visualization en_US


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