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An Ontology Design Pattern for Surface Water Features

  • Gaurav Sinha
  • David Mark
  • Dave Kolas
  • Dalia Varanka
  • Boleslo E. Romero
  • Chen-Chieh Feng
  • E. Lynn Usery
  • Joshua Liebermann
  • Alexandre Sorokine
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8728)

Abstract

Surface water is a primary concept of human experience but concepts are captured in cultures and languages in many different ways. Still, many commonalities exist due to the physical basis of many of the properties and categories. An abstract ontology of surface water features based only on those physical properties of landscape features has the best potential for serving as a foundational domain ontology for other more context-dependent ontologies. The Surface Water ontology design pattern was developed both for domain knowledge distillation and to serve as a conceptual building-block for more complex or specialized surface water ontologies. A fundamental distinction is made in this ontology between landscape features that act as containers (e.g., stream channels, basins) and the bodies of water (e.g., rivers, lakes) that occupy those containers. Concave (container) landforms semantics are specified in a Dry module and the semantics of contained bodies of water in a Wet module. The pattern is implemented in OWL, but Description Logic axioms and a detailed explanation is provided in this paper. The OWL ontology will be an important contribution to Semantic Web vocabulary for annotating surface water feature datasets. Also provided is a discussion of why there is a need to complement the pattern with other ontologies, especially the previously developed Surface Network pattern. Finally, the practical value of the pattern in semantic querying of surface water datasets is illustrated through an annotated geospatial dataset and sample queries using the classes of the Surface Water pattern.

Keywords

Water Body Pour Point Stream Segment Semantic Interoperability Spatial Data Infrastructure 
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 International Publishing Switzerland 2014

Authors and Affiliations

  • Gaurav Sinha
    • 1
  • David Mark
    • 2
  • Dave Kolas
    • 3
  • Dalia Varanka
    • 4
  • Boleslo E. Romero
    • 5
  • Chen-Chieh Feng
    • 6
  • E. Lynn Usery
    • 4
  • Joshua Liebermann
    • 7
  • Alexandre Sorokine
    • 8
  1. 1.Department of GeographyOhio UniversityAthensUSA
  2. 2.Department of GeographyUniversity at BuffaloBuffaloUSA
  3. 3.Raytheon BBN TechnologiesColumbiaUSA
  4. 4.U.S. Geological SurveyRollaUSA
  5. 5.Department of GeographyUniversity of CaliforniaSanta BarbaraUSA
  6. 6.Department of GeographyNational University of SingaporeSingapore
  7. 7.Tumbling Walls LLCNewtonUSA
  8. 8.Oak Ridge National LaboratoryOak RidgeUSA

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