View source for OntologySummit2012 Applications Synthesis

= [[OntologySummit2012|Ontology Summit 2012]]: (Track-4) "Large-scale domain applications" Synthesis  =

== Mission Statement  ==

'''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.''' 

see also: [[OntologySummit2012_Applications_CommunityInput]] 

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== In implemented systems, ontologies are...  ==

* Strong for: 
** Supporting change and aggregation 
** Enabling community aggregation, annotation 
** Automated data ingestion 
** Data validation 
** Ensuring consistency of terms across many data sets (Distributed systems) 
** Supporting reasoning 
** Self describing systems 
** Systems with many complex constraints, rules, laws, with frequent changes (Dynamically changing systems) 
** Data mining / semantic signature extraction 
** Rapid system building 
* Weak for: 
** Being understandable by software engineers and customers 
** Query performance (compared to relational databases) 

== Needs  ==

* Need better standards for common elements: 
** Datatypes 
** Ontology patterns (e.g. whole/part patterns) 
** Collect ontological primitives from observation data 
* Need repositories 
** Repositories of ontological patterns could be more useful than repositories of ontologies 
* Need industrial strength semantic services resident in the cloud 
* Need better visualization tools and approaches 
* Need better tools to help interpret legacy systems, transform into semantic systems. 
* Need to establish feedback mechanisms from end users to ontology designers directly from point of use. 

== Recommendations  ==

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

== Other Observations / Lessons learned  ==

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

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maintained by the Track-4 champions: [[SteveRay|Steve Ray]] & [[TrishWhetzel|Trish Whetzel]] ... please do not edit 

[[Category:OntologySummit]]    [[Category:OntologySummit2012]]    [[Category:OntologyDrivenApplications]]