Interoperability, the measure of how well disparate networks are able to communicate, is woven into the very fabric of the ‘web of data’, the so-called, Semantic Web (SW). As its name suggests, the SW uses ‘semantics’ to facilitate interoperability between islands of seemingly unrelated knowledge. First up, I will outline the Semantic Web in part 1, and in part 2, I will describe ‘semantics’ and how they are used to facilitate interoperability.
The Semantic Web
Sir Tim Berners-Lee, conceived the global SW, a web of linked data, with the same basic architecture as the existing World Wide Web (WWW). Consequently, both webs can co-habit and mesh together. However, there is one big difference between the two webs. Unlike the WWW which is organised for human consumption, the SW is entirely machine-readable. The SW is made up of linked data called ‘resources’. Resources can be anything under the Sun, including concrete entities like people, and abstract entities like thoughts and ideas.
Linking of data
Because machines don’t handle surprises very well resources are organised into a ridged Resource Description Framework (RDF) which provides a predictable linking structure on the SW. How are resources linked? Resources are linked by common relationships. For example, a resource called ‘Bob’ may be linked to another resource called ‘Alice’ by a relationship called ‘knows’. So using two resources and their relationship, a tiny bit of knowledge is made; Bob knows Alice.
Berners-Lee’s idea that the usefulness of resources is enhanced by linking to ‘better’ resources underpins the SW. So, if enough resources are linked, they form a kind of ‘map of knowledge’. The SW contains billions of these maps, called ‘domain ontologies’. You can imagine ontologies are like islands of specific knowledge floating in a sea of resources such as documents, pictures, databases and descriptions. So, like islands, ontologies are ok by themselves but they are much more enhanced if ‘trade routes’ link to other islands and resources.
A domain ontology is a ‘snapshot’ or abstract of some part of human reality. The snapshot is constructed by linking resources and their relationships, the more resources and relationships, the sharper the focus. To this end, resources are always looking for similar resources to connect to, they use the gravity of their relationships to pull together and form new ontologies, like galaxies after the big bang. Suddenly, they are machine-readable snapshots of human reality on the SW that machines can ‘read’ and analyse.
Meh, so what?
In the hospital setting, domain ontologies may describe hidden nursing knowledge and processes. Because ontologies are machine-readable, robots called ‘intelligent agents’ can analyse hospital units such as surgical, emergency or administration looking for dependencies or errors in the logic of the unit. The analysis of ontologies will save time and money by opening the door to automated auditing, freeing up nurses. Also, nurses will use ontologies to add and subtract resources and interventions in a unit which will provide enhanced efficiencies and better patient outcomes.
In the next installment, we will look at semantics in the context of the SW and how semantics facilitate interoperability by connecting resources together to describe even larger ontologies such as a hospital.