Citation
Jackie Man-Kit Leung, Thomas Mandl, Edward A. Lee, Beth Osyk, Charles Shelton, Stavros Tripakis, Ben Lickly. "Scalable Semantic Annotation using Lattice-based Ontologies". 12th International Conference on Model Driven Engineering Languages and Systems, ACM/IEEE, 393-407, 8, October, 2009; (recipient of the MODELS 2009 Distinguished Paper Award).

Abstract
Including semantic information in models helps to expose modeling errors early in the design process, engage a designer in a deeper understanding of the model, and standardize concepts and terminology across a development team. It is impractical, however, for model builders to manually annotate every modeling element with semantic properties. This paper demonstrates a correct, scalable and automated method to infer semantic properties using lattice-based ontologies, given relatively few manual annotations. Semantic concepts and their relationships are formalized as a lattice, and relationships within and between components are expressed as a set of constraints and acceptance criteria relative to the lattice. Our inference engine automatically infers properties wherever they are not explicitly specified. Our implementation leverages the infrastructure in the Ptolemy II type system to get efficient and scalable inference and consistency checking. We demonstrate the approach on a non-trivial Ptolemy II model of an adaptive cruise control system.

Electronic downloads

• ModelOntology_MODELS2009_PrepubVersion.pdf · application/pdf · 1239 kbytes
• http://dx.doi.org/10.1007/978-3-642-04425-0_31. ((Published link))

• HTML

  Jackie Man-Kit Leung, Thomas Mandl, Edward A. Lee, Beth
  Osyk, Charles Shelton, Stavros Tripakis, Ben Lickly. <a
  href="http://chess.eecs.berkeley.edu/pubs/611.html"
  >Scalable Semantic Annotation using Lattice-based
  Ontologies</a>, 12th International Conference on Model
  Driven Engineering Languages and Systems, ACM/IEEE, 393-407,
  8, October, 2009; (recipient of the MODELS 2009
  Distinguished Paper Award).

• Plain text

  Jackie Man-Kit Leung, Thomas Mandl, Edward A. Lee, Beth
  Osyk, Charles Shelton, Stavros Tripakis, Ben Lickly.
  "Scalable Semantic Annotation using Lattice-based
  Ontologies". 12th International Conference on Model
  Driven Engineering Languages and Systems, ACM/IEEE, 393-407,
  8, October, 2009; (recipient of the MODELS 2009
  Distinguished Paper Award).

• BibTeX

  @inproceedings{LeungMandlLeeOsykSheltonTripakisLickly09_ScalableSemanticAnnotationUsingLatticebasedOntologies,
      author = {Jackie Man-Kit Leung and Thomas Mandl and Edward
                A. Lee and Beth Osyk and Charles Shelton and
                Stavros Tripakis and Ben Lickly},
      title = {Scalable Semantic Annotation using Lattice-based
                Ontologies},
      booktitle = {12th International Conference on Model Driven
                Engineering Languages and Systems},
      organization = {ACM/IEEE},
      pages = {393-407},
      day = {8},
      month = {October},
      year = {2009},
      note = {(recipient of the MODELS 2009 Distinguished Paper
                Award)},
      abstract = {Including semantic information in models helps to
                expose modeling errors early in the design
                process, engage a designer in a deeper
                understanding of the model, and standardize
                concepts and terminology across a development
                team. It is impractical, however, for model
                builders to manually annotate every modeling
                element with semantic properties. This paper
                demonstrates a correct, scalable and automated
                method to infer semantic properties using
                lattice-based ontologies, given relatively few
                manual annotations. Semantic concepts and their
                relationships are formalized as a lattice, and
                relationships within and between components are
                expressed as a set of constraints and acceptance
                criteria relative to the lattice. Our inference
                engine automatically infers properties wherever
                they are not explicitly specified. Our
                implementation leverages the infrastructure in the
                Ptolemy II type system to get efficient and
                scalable inference and consistency checking. We
                demonstrate the approach on a non-trivial Ptolemy
                II model of an adaptive cruise control system. },
      URL = {http://chess.eecs.berkeley.edu/pubs/611.html}
  }
  

Posted by Christopher Brooks on 4 Aug 2009.
Groups: pthomas
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