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Merging Sets of Taxonomically Organized Data Using Concept Mappings under Uncertainty

  • David Thau
  • Shawn Bowers
  • Bertram Ludäscher
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5871)

Abstract

We present a method for using aligned ontologies to merge taxonomically organized data sets that have apparently compatible schemas, but potentially different semantics for corresponding domains. We restrict the relationships involved in the alignment to basic set relations and disjunctions of these relations. A merged data set combines the domains of the source data set attributes, conforms to the observations reported in both data sets, and minimizes uncertainty introduced by ontology alignments. We find that even in very simple cases, merging data sets under this scenario is non-trivial. Reducing uncertainty introduced by the ontology alignments in combination with the data set observations often results in many possible merged data sets, which are managed using a possible worlds semantics. The primary contributions of this paper are a framework for representing aligned data sets and algorithms for merging data sets that report the presence and absence of taxonomically organized entities, including an efficient algorithm for a common data set merging scenario.

Keywords

Compress Function Ontology Concept Context Attribute Naive Algorithm Ontology Alignment 
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 2009

Authors and Affiliations

  • David Thau
    • 1
  • Shawn Bowers
    • 2
  • Bertram Ludäscher
    • 1
    • 2
  1. 1.Dept. of Computer ScienceUniversity of California Davis
  2. 2.Genome CenterUniversity of California Davis

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