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Detecting similarities in antipattern ontologies using semantic social networks: implications for software project management

Published online by Cambridge University Press:  01 September 2009

Dimitrios L. Settas ,
Sulayman K. Sowe  and
Ioannis G. Stamelos
Dimitrios L. Settas
Affiliation:
Department of Informatics, Aristotle University of Thessaloniki, 541 24 Thessaloniki, Greece; e-mail: dsettas@csd.auth.gr, sksowe@csd.auth.gr, stamelos@csd.auth.gr
Sulayman K. Sowe
Affiliation:
Department of Informatics, Aristotle University of Thessaloniki, 541 24 Thessaloniki, Greece; e-mail: dsettas@csd.auth.gr, sksowe@csd.auth.gr, stamelos@csd.auth.gr
Ioannis G. Stamelos
Affiliation:
Department of Informatics, Aristotle University of Thessaloniki, 541 24 Thessaloniki, Greece; e-mail: dsettas@csd.auth.gr, sksowe@csd.auth.gr, stamelos@csd.auth.gr
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Abstract

Ontology has been recently proposed as an appropriate formalism to model software project management antipatterns, in order to encode antipatterns in a computer understandable form and introduce antipatterns to the Semantic Web. However, given two antipattern ontologies, the same entity can be described using different terminology. Therefore, the detection of similar antipattern ontologies is a difficult task. In this paper, we introduce a three-layered antipattern semantic social network, which involves the social network, the antipattern ontology network and the concept network. Social Network Analysis (SNA) techniques can be used to assist software project managers in finding similar antipattern ontologies. For this purpose, SNA measures are extracted from one layer of the semantic social network to another and this knowledge is used to infer new links between antipattern ontologies. The level of uncertainty associated with each new link is represented using Bayesian Networks (BNs). Furthermore, BNs address the issue of quantifying the uncertainty of the data collected regarding antipattern ontologies for the purposes of the conducted analysis. Finally, BNs are used to augment SNA by taking into account meta-information in their calculations. Hence, other knowledge not included in the social network can be used in order to search the social network for further inference. The benefits of using an antipattern semantic social network are illustrated using an example community of software project management antipattern ontologies.

Type
Original Article
Information
The Knowledge Engineering Review , Volume 24 , Special Issue 3: Software and system engineering – part 2 , September 2009 , pp. 287 - 307
Copyright
Copyright © Cambridge University Press 2009

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