Thoughts on KOS (Part1): Getting to grips with “semantic” interoperability
Enabling and managing interoperability at the data and the service level is one of the strategic key issues in networked knowledge organization systems (KOSs) and a growing issue in effective data management. But why do we need “semantic” interoperability and how can we achieve it?
Interoperability vs. Integration
The concept of (data) interoperability can best be understood in contrast to (data) integration. While integration refers to a process, where formerly distinct data sources and their representation models are being merged into one newly consolidated data source, the concept of interoperability is defined by a structural separation of knowledge sources and their representation models, but that allows connectivity and interactivity between these sources by deliberately defined overlaps in the representation model. Under circumstances of interoperability data sources are being designed to provide interfaces for connectivity to share and integrate data on top of a common data model, while leaving the original principles of data and knowledge representation intact. Thus, interoperability is an efficient means to improve and ease integration of data and knowledge sources.
Three levels of interoperability
When designing interoperable KOSs it is important to distinguish between structural, syntactic and semantic interoperability (Galinski 2006):
- Structural interoperability is achieved by representing metadata using a shared data model like the Dublin Core Abstraction Model or RDF (Resource Description Framework).
- Syntactic interoperability if achieved by serializing data in a shared mark-up language like XML, Turtle or N3.
- Semantic interoperability is achieved by using a shared terminology or controlled vocabulary to label and classify metadata terms and relations.
Given the fact that metadata standards carry a lot of intrinsic legacy, it is sometimes very difficult to achieve interoperability at all three levels mentioned above. Metadata formats and models are historically grown, they are most of the time a result of community decision processes, often highly formalized for specific functional purposes and most of the time deliberately rigid and difficult to change. Hence it is important to have a clear understanding and documentation of the application profile of a metadata format as a precondition for enabling interoperability at all three levels mentioned above. Semantic Web standards do a really good job in this respect!!
In the next post, we will take a look at various KOSs and how they differ with respect to expressivity, scope and target group.