Knowledge representation: logical, philosophical and computational foundations
Knowledge representation: logical, philosophical and computational foundations
ACM SIGSOFT Software Engineering Notes
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
Building large knowledge bases by mass collaboration
Proceedings of the 2nd international conference on Knowledge capture
Using Bayesian Belief Networks to Model Software Project Management Antipatterns
SERA '06 Proceedings of the Fourth International Conference on Software Engineering Research, Management and Applications
Uncertainty and the Semantic Web
IEEE Intelligent Systems
Antipatterns in the Creation of Intelligent Systems
IEEE Intelligent Systems
The Most Important Service-Oriented Antipatterns
ICSEA '07 Proceedings of the International Conference on Software Engineering Advances
Web Semantics: Science, Services and Agents on the World Wide Web
SPARSE: A symptom-based antipattern retrieval knowledge-based system using Semantic Web technologies
Expert Systems with Applications: An International Journal
Towards Automatic Generation of Ontology-Based Antipattern Bayesian Network Models
SERA '11 Proceedings of the 2011 Ninth International Conference on Software Engineering Research, Management and Applications
A bayesian network approach to ontology mapping
ISWC'05 Proceedings of the 4th international conference on The Semantic Web
Expert Systems with Applications: An International Journal
An ontology based e-learning system using antipatterns
ICWL'11 Proceedings of the 10th international conference on Advances in Web-Based Learning
Hi-index | 12.05 |
Antipatterns provide information on commonly occurring solutions to problems that generate negative consequences. The antipattern ontology has been recently proposed as a knowledge base for SPARSE, an intelligent system that can detect the antipatterns that exist in a software project. However, apart from the plethora of antipatterns that are inherently informal and imprecise, the information used in the antipattern ontology itself is many times imprecise or vaguely defined. For example, the certainty in which a cause, symptom or consequence of an antipattern exists in a software project. Taking into account probabilistic information would yield more realistic, intelligent and effective ontology-based applications to support the technology of antipatterns. However, ontologies are not capable of representing uncertainty and the effective detection of antipatterns taking into account the uncertainty that exists in software project antipatterns still remains an open issue. Bayesian Networks (BNs) have been previously used in order to measure, illustrate and handle antipattern uncertainty in mathematical terms. In this paper, we explore the ways in which the antipattern ontology can be enhanced using Bayesian networks in order to reinforce the existing ontology-based detection process. This approach allows software developers to quantify the existence of an antipattern using Bayesian networks, based on probabilistic knowledge contained in the antipattern ontology regarding relationships of antipatterns through their causes, symptoms and consequences. The framework is exemplified using a Bayesian network model of 13 antipattern attributes, which is constructed using BNTab, a plug-in developed for the Protege ontology editor that generates BNs based on ontological information.