TY - GEN
T1 - Challenges of identifying communities with shared semantics in enterprise modeling
AU - van der Linden, Dirk
AU - Hoppenbrouwers, Stijn
PY - 2012/1/1
Y1 - 2012/1/1
N2 - In this paper we discuss the use and challenges of identifying communities with shared semantics in Enterprise Modeling. People tend to understand modeling meta-concepts (i.e., a modeling language's constructs or types) in a certain way and can be grouped by this understanding. Having an insight into the typical communities and their composition (e.g., what kind of people constitute a semantic community) would make it easier to predict how a conceptual modeler with a certain background will generally understand the meta-concepts he uses, which is useful for e.g., validating model semantics and improving the efficiency of the modeling process itself. We demonstrate the use of psychometric data from two studies involving experienced (enterprise) modeling practitioners and computing science students to find such communities, discuss the challenge that arises in finding common real-world factors shared between their members to identify them by and conclude that the common (often implicit) grouping properties such as similar background, focus and modeling language are not supported by empirical data.
AB - In this paper we discuss the use and challenges of identifying communities with shared semantics in Enterprise Modeling. People tend to understand modeling meta-concepts (i.e., a modeling language's constructs or types) in a certain way and can be grouped by this understanding. Having an insight into the typical communities and their composition (e.g., what kind of people constitute a semantic community) would make it easier to predict how a conceptual modeler with a certain background will generally understand the meta-concepts he uses, which is useful for e.g., validating model semantics and improving the efficiency of the modeling process itself. We demonstrate the use of psychometric data from two studies involving experienced (enterprise) modeling practitioners and computing science students to find such communities, discuss the challenge that arises in finding common real-world factors shared between their members to identify them by and conclude that the common (often implicit) grouping properties such as similar background, focus and modeling language are not supported by empirical data.
KW - community identification
KW - conceptual understanding
KW - enterprise modeling
KW - personal semantics
KW - semantics clustering
U2 - 10.1007/978-3-642-34549-4_12
DO - 10.1007/978-3-642-34549-4_12
M3 - Conference contribution
AN - SCOPUS:84868359409
SN - 9783642345487
T3 - Lecture Notes in Business Information Processing
SP - 160
EP - 171
BT - The Practice of Enterprise Modeling - 5th IFIP WG 8.1 Working Conference, PoEM 2012, Proceedings
PB - Springer
T2 - 5th IFIP WG 8.1 Working Conference on the Practice of Enterprise Modeling, PoEM 2012
Y2 - 7 November 2012 through 8 November 2012
ER -