Visualisation for ontology sense-making: A tree-map based algorithmic approach

Ontology sense-making or visual comprehension of the ontological schemata and structure are vital for cross-validation purposes of the ontology increment during the process of applied ontology construction. Also, it is important to query the ontology in order to verify the accuracy of the stored knowledge embeddings. This will boost the interactions between domain specialists and ontologists in applied ontology construction processes. Hence existing mechanisms have numerous of deficiencies (discussed in the paper), a new algorithm is proposed in this research to boost the efficiency of usage of tree-maps for effective ontology sense making. Proposed algorithm and prototype are quantitatively and qualitatively assessed for their accuracy and efficacy.

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Ontology specific visual canvas generation to facilitate sense-making-an algorithmic approach

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v12i2.pp1818-1830 ◽

2022 ◽

Vol 12 (2) ◽

pp. 1818

Author(s):

Kaneeka Vidanage ◽

Noor Maizura Mohamad Noor ◽

Rosmayati Mohemad ◽

Zuriana Abu Bakar

Keyword(s):

Sense Making ◽

Algorithmic Approach ◽

Domain Specific ◽

Machine Readable ◽

Novel Algorithm

Ontologies are domain-specific conceptualizations that are both human and machine-readable. Due to this remarkable attribute of ontologies, its applications are not limited to computing domains. Banking, medicine, agriculture, and law are a few of the non-computing domains, where ontologies are being used very effectively. When creating ontologies for non-computing domains, involvement of the non-computing domain specialists like bankers, lawyers, farmers become very vital. Hence, they are not semantic specialists, particularly designed visualization assistance is required for the ontology schema verifications and sense-making. Existing visualization methods are not fine-tuned for non-technical domain specialists and there are lots of complexities. In this research, a novel algorithm capable of generating domain specialists’ friendlier visualization canvas has been explored. This proposed algorithm and the visualization canvas has been tested for three different domains and overall success of 85% has been yielded.

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Cross validation of a structural relationship model of student performance

Journal of Dental Education ◽

10.1002/j.0022-0337.1986.50.9.tb02040.x ◽

1986 ◽

Vol 50 (9) ◽

pp. 545-548

Author(s):

RH Potter

Keyword(s):

Student Performance ◽

Cross Validation ◽

Structural Relationship ◽

Relationship Model

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Periodic Splines for Spectral Density Estimation: The Use of Cross Validation for Determining the Degree of Smoothing

Communications in Statistics - Simulation and Computation ◽

10.1080/03610917508548343 ◽

1975 ◽

Vol 4 (2) ◽

pp. 125-141

Author(s):

G. Wahba ◽

S. Wold

Keyword(s):

Spectral Density ◽

Density Estimation ◽

Cross Validation ◽

Spectral Density Estimation

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Item Bias Analysis of the Young Schema-Questionnaire for Psychopathology, Gender, and Educational Level

European Journal of Psychological Assessment ◽

10.1027/1015-5759/a000044 ◽

2011 ◽

Vol 27 (1) ◽

pp. 65-70 ◽

Cited By ~ 11

Author(s):

Marleen M. Rijkeboer ◽

Huub van den Bergh ◽

Jan van den Bout

Keyword(s):

Educational Level ◽

Cross Validation ◽

Item Bias ◽

Response Patterns ◽

Dutch Population ◽

Bias Analysis ◽

Young Schema Questionnaire ◽

Item Functioning ◽

Item Level ◽

Schema Questionnaire

This study examines the construct validity of the Young Schema-Questionnaire at the item level in a Dutch population. Possible bias of items in relation to the presence or absence of psychopathology, gender, and educational level was analyzed, using a cross-validation design. None of the items of the YSQ exhibited differential item functioning (DIF) for gender, and only one item showed DIF for educational level. Furthermore, item bias analysis did not identify DIF for the presence or absence of psychopathology in as much as 195 of the 205 items comprising the YSQ. Ten items, however, spread over the questionnaire, were found to yield relatively inconsistent response patterns for patients and nonclinical participants.

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