Enable Interoperability

Background

There are two major search problems addressed by semantic interoperabilityThe ability of multiple systems to exchange information in useful ways; in particular, the ability for each system to 'understand' the terms of the other sufficiently to use those terms correctly. Related Guide between data systems:

1. We cannot find all the data we are seeking.
2. We get too many results and they are difficult to classify.

How Ontologies Can Help

Automated tools can use ontologies for such services as more accurate web search, intelligent software agents, and knowledge management. By formalizing relations between concepts of one or more collections in a machine-readableIn the context of metadata, formatted in a way that is well defined and processable by the system's software and hardware. Metadata with this characteristic can be discovered, ingested, and presented by an electronic system (also known as 'computable'). Related Guide language, ontologies can facilitate interoperability.

These concept descriptions determine the format in which the information is kept, and establish the actual conceptual information, or semantic content, that is defined in the ontology. Agreements should also be reached about the community and technical processes used to modify the ontology. Finally, ontologies are designed to be computer-usable (also known as "computable") - their format and rules are specified so that the information can be found, exchanged, and applied by computer systems, without additional human intervention.

Some examples of how ontologies can faciltate interoperability:

• Mappings Between Controlled VocabulariesA managed list of terms. In the context of vocabularies, management typically includes careful selection of terms, maintenance of terms over time (i.e. addition, deprecation, modification), and presentation of the vocabulary in an accessible format. Related Guide
• Controlled vocabularies are important, but there is rarely only one controlled vocabulary relevant to a domain of interest. Different funding sources, project purposes, program histories, etc., lead to different controlled vocabularies for a given domain. Mappings between controlled vocabularies, normalized in ontology representation languages such as the Resource Description Framework or the Web Ontology Language (OWL), can consist of identifying terms in each vocabulary as equivalent to, broader than, narrower than, or a subclass of terms in another vocabulary. Such ontology representations and mappings can enhance interoperability between data systems in that the use of specific search terms is no longer necessary. The mappings between terms in different controlled vocabularies used in different data systems can allow the user to find additional information. For example, at the MMIMarine Metadata Interoperability Advancing Domain Vocabularies Workshop in 2005, we demonstrated the enhanced ability to quickly find sea surface temperature data sources (regardless of whether "SSTSea Surface Temperature ", "sea surface temperature", "Ocean Temperature" variations were used), using an MMI semantic mediation service called Semor. Semor is a semantic mediation service for earth science terminologies. Terminologies are expressed in ontologies following the RDF model. Users can query terminologies using RDF query languages or simple text matching queries. This service helps users discover what a term means and its relationshipsConnections between metadata terms within a vocabulary. These relationships can connect terms by scope, provenance, or other well-defined criteria. to other terms.
• Mappings Between Categories/Hierarchies of Concepts

TaxonomiesA multi-level controlled vocabulary in which metadata terms are grouped according to subject-specific classes, usually hierarchical. Related Guide (or other hierarchies) used by different data systems, as well as within a data system, may vary. Ontologies, and mappings between ontologies, can facilitate interoperability between these higher-level categorizations. For example, the Oregon Coastal Atlas and the Marine Irish Digital Atlas, which interoperate as components of an International Coastal Atlas Network, use different classifications for grouping their mapping data sets to help users find data sets of interest. MMI is working with this group to create an interoperability prototype between the two atlases, using an upper ontology, as well as mappings between classifications and terms.