Research and Data Reusability

Ontologies constitute a key technology enabling a wide range of data services. The growing availability of data has shifted the focus from closed, relatively data-poor applications, to mechanisms and applications for searching, integrating, and making use of the vast amounts of data that are now available. Ontologies provide the semantic underpinning that enables reuse of research data. Current research is exploring the use of formal ontologies for specifying content-specific agreements for a variety of data/knowledge reuse activities.

A community of practice has to establish its own domain of discourse and choose a formalism, i.e., a knowledge representation language, in order to create its own domain-specific ontology. In addition, a set of linguistic terms by which the members of the community will refer to these objects must be identified. Building this set of terms is difficult because words often have multiple synonyms and because the meanings of words in natural language always depend heavily on the contexts in which the words are used. To overcome this difficulty, explicit lexicons should be created which offer the members of a community of practice a set of terms with which to refer to specific concepts.

In the context of a networked scientific world, domain-specific ontologies are not standalone artifacts. They relate to each other in ways that might affect their meaning, and are inherently distributed in a network of interlinked semantic resources, taking into account in particular their dynamics, modularity, and contextual dependencies. The alignment of domain-specific ontologies is crucial for data reusability. It is achieved through a set of mapping rules that specify a correspondence between various entities, such as objects, concepts, relations, and instances. Several concept and relation constructors are offered to construct complex expressions to be used in mappings.