Retrieval of Semantic Workflows with Knowledge Intensive Similarity Measures

  • Ralph Bergmann
  • Yolanda Gil
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6880)

Abstract

We describe a new model for representing semantic workflows as semantically labeled graphs, together with a related model for knowledge intensive similarity measures. The application of this model to scientific and business workflows is discussed. Experimental evaluations show that similarity measures can be modeled that are well aligned with manual similarity assessments. Further, new algorithms for workflow similarity computation based on A* search are described. A new retrieval algorithm is introduced that goes beyond traditional sequential retrieval for graphs, interweaving similarity computation with case selection. Significant reductions on the overall retrieval time are demonstrated without sacrificing the correctness of the computed similarity.

Keywords

Similarity Measure Aggregation Function Priority Queue Retrieval Performance Decision Tree Modeler 
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 2011

Authors and Affiliations

  • Ralph Bergmann
    • 1
  • Yolanda Gil
    • 2
  1. 1.Department of Business Information Systems IIUniversity of TrierTrierGermany
  2. 2.Information Sciences InstituteUniversity of Southern CaliforniaMarina del ReyUSA

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