Knowledge Exploitation via Ontology Development in e-Government Project Management

Sarantis, Demetrios; Askounis, Dimitris

doi:10.20533/ijds.2040.2570.2010.0030

Cited by 3 publications

(2 citation statements)

References 15 publications

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“…Although the website is generally very well received and appreciated, (See e.g. [16]) it was underlined that the creation of such a website is not simple, and complex issues of integration and interoperability, security, and information architecture must to be taken into consideration [17].…”

Section: Innovations In Public Sector Reportingmentioning

confidence: 99%

How to Be Open About Spending: Innovating in Public Sector Reporting in the Information Age

Faber

Budding

2018

Advances in Intelligent Systems and Computing

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Section: Innovations In Public Sector Reportingmentioning

confidence: 99%

How to Be Open About Spending: Innovating in Public Sector Reporting in the Information Age

Faber

Budding

2018

Advances in Intelligent Systems and Computing

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“…The concept Government is subconcept of Project and its subconcepts are government organization and municipality. Project related ontology with knowledge about e-government project types is presented in (Sarantis and Askounis, 2010). Out of competency questions referring to types of e-government projects and the way they relate to e-government dimensions the following concepts have been defined: beneficiary, administration level, domain sector, function and nature.…”

Section: E-governance Ontologiesmentioning

confidence: 99%

Classification of text documents supervised by domain ontologies

Rozeva¹

2012

ATI

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The research objective is to establish an approach for supporting the classification of text documents referring to a specified domain. The focus is on the preliminary topic assignment to the documents used for training the model. The method implements domain ontology as background knowledge. The idea consists in extracting the preliminary topics for training the classifier by means of unsupervised machine learning on a text corpus and further alignment of the document vectors to concepts of the ontology. The results obtained by classification of new documents supervised by e-governance ontology with several machine learning algorithms showed sufficient match of their content to the ontology concepts. A conclusion is drawn that the approach can support the automatic extraction of documents relevant to any domain described by ontology.

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