Data as Knowledge

Abstraction creates opportunities to describe and discern new relationships within content, build linkages between objects and through connected devices.

Data Abstraction allows the handling of data in different ways. This can be used to increase the efficiency of handling the data through an Ontology to effectively combine the data or information from multiple sources and formally represent knowledge within a domain. This can be organised by ‘people, ‘places’ and ‘organisations’.

The use of an Ontology enables the unambiguous identification of entities across multiple sources of data through the explicit definitions of terms and relationships. Verification of these mappings can be either through direct user specification or automated across a system.

Without automation of these processes the vast volume of latent knowledge within data is unlikely to be extracted, as the costs would be prohibitive.

By using formal models of representation, with explicit definitions of concepts and naming of relationships, linking diverse content is possible. Linking this content supports Semantic Integration by acting as an intermediary. This can automate the communication and linkages between different sources of information through semantics.

This approach creates opportunities by making this available via search and discovery; improved knowledge representation and publishing across connected devices as Linked Data can be leveraged for process improvement(s) within organisations and ‘productisation’ of knowledge. 

The redistribution of knowledge through Semantic Web techniques unlocks a range of opportunities for product and application development but also improves efficiency across information retrieval, content distribution, research and collaboration.