Science Environment for Ecological Knowledge
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Science Environment for Ecological Knowledge




SMS Meeting Notes_10_May_2003

Semantic Mediation System - May 10, 2003, SDSC


  • Bertram Ludaescher
  • Shawn Bowers
  • Joseph Goguen
  • Mark Schildhauer
  • Matt Jones
  • Trevor Paterson
  • Jessie Kennedy
  • Dave Vieglais
  • Arcot Rajasekar
  • Jenny Wang
  • Liying Sui
  • Victor Vianu
  • Dave Stockwell
  • Ferdinando Villa
  • Chad Berkley
  • Rich Williams

Notes from Ludaescher Overview of SMS

  • See slides for his presentation

  • Goguen: notes that we don't want to expose OWL or XML Schema directly to users, need a UI tool for them to create and interact with Ontologies
  • Ludaescher: Need example of more complex data type conversions (e.g., abundance to occurence, density to occurence, abundance/area to density)
  • Ludaescher: Gene Ontology is good example but simple, some difficulty with more advanced usages
  • Jones: GO experience highlights difficulties of generating ontologies in a community
  • Ludaescher: OWL Lite, OWL DL, and OWL Full are primary candidates, need to discuss wrt requirements
  • Kennedy: How does SMS integrate with taxonomy name server? Can they share some infrastructure?
  • Villa: Other system components will be able to use it too, e.g., place name resolution
  • Villa: We'll need to compile a list of relevant applications of SMS to SEEK

Vianu: XML Schema and validation

  • See slides for the presentation

  • tree automata theory can be applied to various scheam transformation
  • applicable to static analysis for step integration and data integration
  • see NASA Amphion for planning transformations

Wang: Integration of Heterogeneous XML Data

  • see slides for the presentation

  • demonstrated DDXMI system for integrating XML data from different document types based on a user-generated mapping to a global DTD


KR tasks

  1. prototype unit conversion reasoning and query approaches (Ludaescher, Berkley)
  2. generate working example/use case of unit conversion (how it fits with browsers)
  3. generate examples of semantic type info for species prediction pipeline(s)
  4. generate examples of semantic types based on metadata stored in EML (focused on productivity)
  5. model (formalize) the "semantic type" discussion
  6. develop (alpha) ontology to use in tagging "semantic types" for our case studies
  7. prototype "smart query" based on ontology expansion/thesauri function
  8. Generate complete EML descriptions of real data with appropriate unitDictionary and semantic type labels for attributes

Ecogrid tasks

  1. Develop GRID service interface that extends current query to include the "smart discovery" feature

Taxon tasks

  1. Resolve where taxonomy name resolution servive fits in with KR and SMS ("aboveground biomass" implies you are working with plants)

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This page last changed on 28-Jun-2004 13:26:32 PDT by LTER.stekell.