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.

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

--

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

Ontolog Forum

Contents