TY - GEN
T1 - A conceptual modeling framework for expressing observational data semantics
AU - Bowers, Shawn
AU - Madin, Joshua S.
AU - Schildhauer, Mark P.
PY - 2008
Y1 - 2008
N2 - Observational data (i.e., data that records observations and measurements) plays a key role in many scientific disciplines. Observational data, however, are typically structured and described in ad hoc ways, making its discovery and integration difficult. The wide range of data collected, the variety of ways the data are used, and the needs of existing analysis applications make it impractical to define "one-size-fits-all" schemas for most observational data sets. Instead, new approaches are needed to flexibly describe observational data for effective discovery and integration. In this paper, we present a generic conceptual-modeling framework for capturing the semantics of observational data. The framework extends standard conceptual modeling approaches with new constructs for describing observations and measurements. Key to the framework is the ability to describe observation context, including complex, nested context relationships. We describe our proposed modeling framework, focusing on context and its use in expressing observational data semantics.
AB - Observational data (i.e., data that records observations and measurements) plays a key role in many scientific disciplines. Observational data, however, are typically structured and described in ad hoc ways, making its discovery and integration difficult. The wide range of data collected, the variety of ways the data are used, and the needs of existing analysis applications make it impractical to define "one-size-fits-all" schemas for most observational data sets. Instead, new approaches are needed to flexibly describe observational data for effective discovery and integration. In this paper, we present a generic conceptual-modeling framework for capturing the semantics of observational data. The framework extends standard conceptual modeling approaches with new constructs for describing observations and measurements. Key to the framework is the ability to describe observation context, including complex, nested context relationships. We describe our proposed modeling framework, focusing on context and its use in expressing observational data semantics.
KW - ONTOLOGIES
UR - http://www.scopus.com/inward/record.url?scp=57049134672&partnerID=8YFLogxK
U2 - 10.1007/978-3-540-87877-3_5
DO - 10.1007/978-3-540-87877-3_5
M3 - Conference proceeding contribution
AN - SCOPUS:57049134672
SN - 3540878769
SN - 9783540878766
VL - 5231 LNCS
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 41
EP - 54
BT - Conceptual Modeling - ER 2008
A2 - Li, Qing
A2 - Spaccapietra, Stefano
A2 - Yu, Eric
A2 - Olivé, Antoni
PB - Springer, Springer Nature
CY - Berlin, Heidelberg
T2 - 27th International Conference on Conceptual Modeling, ER 2008
Y2 - 20 October 2008 through 24 October 2008
ER -