Modern Information Retrieval
Towards High-Precision Service Retrieval
ISWC '02 Proceedings of the First International Semantic Web Conference on The Semantic Web
Organizing Business Knowledge: The MIT Process Handbook
Organizing Business Knowledge: The MIT Process Handbook
Interestingness measures for data mining: A survey
ACM Computing Surveys (CSUR)
Imprecise RDQL: towards generic retrieval in ontologies using similarity joins
Proceedings of the 2006 ACM symposium on Applied computing
Automated semantic web service discovery with OWLS-MX
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Measuring similarity between semantic business process models
APCCM '07 Proceedings of the fourth Asia-Pacific conference on Comceptual modelling - Volume 67
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
Modeling and Query Patterns for Process Retrieval in OWL
ISWC '09 Proceedings of the 8th International Semantic Web Conference
Searching repositories of web application models
ICWE'10 Proceedings of the 10th international conference on Web engineering
Application and evaluation of inductive reasoning methods for the semantic web and software analysis
RW'11 Proceedings of the 7th international conference on Reasoning web: semantic technologies for the web of data
Textual and Content-Based Search in Repositories of Web Application Models
ACM Transactions on the Web (TWEB)
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The vision of semantic business processes is to enable the integration and inter-operability of business processes across organizational boundaries. Since different organizations model their processes differently, the discovery and retrieval of similar semantic business processes is necessary in order to foster inter-organizational collaborations. This paper presents our approach of using iSPARQL --- our imprecise query engine based on iSPARQL --- to query the OWL MIT Process Handbook --- a large collection of over 5000 semantic business processes. We particularly show how easy it is to use iSPARQL to perform the presented process retrieval task. Furthermore, since choosing the best performing similarity strategy is a non-trivial, data-, and context-dependent task, we evaluate the performance of three simple and two human-engineered similarity strategies. In addition, we conduct machine learning experiments to learn similarity measures showing that complementary information contained in the different notions of similarity strategies provide a very high retrieval accuracy. Our preliminary results indicate that iSPARQL is indeed useful for extending the reach of queries and that it, therefore, is an enabler for inter- and intra-organizational collaborations.