A Conceptual Modeling Framework for Expressing Observational Data Semantics

  • Shawn Bowers
  • Joshua S. Madin
  • Mark P. Schildhauer
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5231)


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.


Observational Data Description Logic Cardinality Constraint Context Relationship Observation Type 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Shawn Bowers
    • 1
  • Joshua S. Madin
    • 2
  • Mark P. Schildhauer
    • 3
  1. 1.Genome CenterUniversity of CaliforniaDavis
  2. 2.Dept. of Biological SciencesMacquarie UniversityAustralia
  3. 3.National Center for Ecological Analysis and Synthesis, UC Santa BarbaraUSA

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