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Ontology Summit 2012: (Track-4) "Large-scale domain applications" Synthesis     (1)

Mission Statement     (1A)

This track will help to ground the discussions in the other tracks and bring key challenges to light by describing current large-scale systems and systems of systems that either use, or could use, ontologies in their deployment. "Large-scale" can mean either very large data sets, very complex data sets, federated systems, highly distributed systems, or real-time, continuous data systems. Examples of large data sets might include scientific observations and studies; complex data sets could be technical data packages for manufactured products, or electronic health records; federated systems could include information sharing to combat terrorism, highly distributed systems includes items such as the smart electrical grid (aka Smart Grid), and real-time systems include network management systems. Of course, some big systems might include all five aspects.     (1A1)


In implemented systems, ontologies are...     (1B)

  • Need better standards for common elements:     (1C1)
  • Need repositories     (1C2)
    • Repositories of ontological patterns could be more useful than repositories of ontologies     (1C2A)
  • Need industrial strength semantic services resident in the cloud     (1C3)
  • Need better visualization tools and approaches     (1C4)
  • Need better tools to help interpret legacy systems, transform into semantic systems.     (1C5)
  • Need to establish feedback mechanisms from end users to ontology designers directly from point of use.     (1C6)

Recommendations     (1D)

  • Look for the 80-20 rule of semantic development     (1D1)
  • Use well defined and narrow use cases to demonstrate benefits of semantic approaches     (1D2)
  • Having explicit vocabularies (classifiers) is a must in a distributed system;     (1D3)
  • Community should be included in the development and evolution of vocabularies     (1D4)
  • It is critical to capture and evolve domain knowledge in a form that the community is comfortable with     (1D5)
  • Transition from implicit domain knowledge to explicit encoding requires community consensus - and an organization to manage the consensus     (1D6)
  • Some have recommended exposing users to SKOS semantics; use more complicated constructs only on back end if necessary.     (1D7)

Other Observations / Lessons learned     (1E)

  • UML to OWL is a common requirement for legacy systems     (1E1)
  • Ontology patterns are very helpful, and encourage model reuse     (1E2)
  • Semantic techniques work best when not compromised by implementation tradeoffs     (1E3)
  • Semantic methods are faster to implement and easier to maintain     (1E4)
  • Semantic approaches particularly suited to systems with many complex constraints, rules, laws, with frequent changes     (1E5)
  • Incremental implementation is possible through federation of datastores     (1E6)
  • Ontologies are not always applied to enable reasoners - sometimes just as a more rigorous data modeling approach     (1E7)
  • Engineers turned ontologists often don't have the necessary background/skills     (1E8)
  • Existing infrastructure supports traditional software development far better than large-scale ontology development     (1E9)
  • There are many ontologies of dubious quality     (1E10)
  • Service-oriented architectures allow separation of code and ontology updates     (1E11)
  • Reasoner and query engine performance is highly dependent upon the exact formulation of rules and queries     (1E12)
  • No single technology/tool currently provides the best solution across all large system use cases     (1E13)

maintained by the Track-4 champions: Steve Ray & Trish Whetzel ... please do not edit     (1E15)


This page has been migrated from the OntologWiki - Click here for original page     (1E16)