Enhancing the semantics of UML association redefinition
Data & Knowledge Engineering
A method for filtering large conceptual schemas
ER'10 Proceedings of the 29th international conference on Conceptual modeling
A tool for filtering large conceptual schemas
ER'11 Proceedings of the 30th international conference on Advances in conceptual modeling: recent developments and new directions
On computing the importance of associations in large conceptual schemas
Conceptual Modelling and Its Theoretical Foundations
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The amount of knowledge represented in the Health Level 7 International (HL7) information models is very large. The sheer size of those models makes them very useful for the communities for which they are developed. However, the size of the models and their overall organization makes it difficult to manually extract knowledge from them. We propose to extract that knowledge by using a novel filtering method that we have developed. Our method is based on the concept of class interest as a combination of class importance and class closeness. The application of our method automatically obtains a filtered information model of the whole HL7 models according to the user preferences. We show that the use of a prototype tool that implements that method and produces such filtered model improves the usability of the HL7 models due to its high precision and low computational time.