In part one, we saw that resources including the largest resource, ontologies, seek out to connect with similar ‘better’ resources on the Semantic Web. For example, say we want to examine the life of ‘Flipper’, the1960’s TV star. We can connect a whole bunch of resources on the Semantic Web which will bring a sharper focus to Flipper’s slice of reality. This connectivity is achievable by using an element called a ‘triple’.
The basic unit of knowledge on the semantic web is the ‘triple’. A triple is like a simple sentence containing subject, predicate and object; ‘Bob knows Alice’ was an example of a triple in part one. The predicate ‘knows’ is the relationship between subject ‘Bob’ and object ‘Alice’.
In Figure 1 we can see triples connecting Flipper to other resources describing his world. Triples easily identifiable in Figure 1 are: ‘Flipper is a dolphin’, ‘Flipper is a animal TV star’, ‘Flipper knows Sandy Ricks’ and ‘Flipper lives at coral key’. Using this simple Flipper example, we can see triples provide a sharper focus on Flipper and his life. Each resource connected to Flipper by a triple contains lots of extra information. The resources in Figure 1 don’t have to be an ontology, they can be anything, including databases, comma delimited documents, triple stores or virtually any resource. Suddenly, you, or a machine, can navigate about Flipper’s life and discover more and more stuff.
Figure 1: Flippers life on the Semantic Web
The Semantic Web naturally lends itself to interoperability because of the Linking Of Data standards embedded in it. The thing is, it doesn’t matter what form or structure the resources come in, disparate resources can be linked on the Semantic Web by triples because triples are independent of the resources. Triples achieve linking by recognising similar semantics in the other resource. Triples are full of semantics.
Semantics, in the context of Linking Of Data, are not just the meaning of one word or phrase; it is the sum of many descriptors associated with a triple. For example:
- Names of concepts (terms)
- Names of relationships
- Any annotation that is placed to describe the concept or relationship for the benefit of humans
- Any constraint which sets the rules of class membership.
A machine or person whose job it is to link ontologies is not limited to the above semantics to link resources together. A machine called a ‘reasoner’ will scan the ontology and infer a relationship between Bob and Alice because Bob and Alice may work in the same hospital unit, share the same individual constraints or belong to the same club. Also, a machine could trawl the Semantic Web ranking the linguistic ‘closeness’ of terms and relationships and automatically link stuff by ranking the probability that resources or people are connected in some way. For nurses, we can take a ‘snapshot’ of a nursing unit and analyse the processes that occur. If we can visualise the processes we can ‘tweak’ them to provide greater efficiencies which flow on to better patient outcomes.
Anyway, things get “curioser and curioser” from here on in. For instance, “how does a machine know what ‘knows’ means in the ‘Bob knows Alice’ triple?”. I was just going to write two articles but I may as well continue down the rabbit hole in upcoming articles. I will explain the ‘knows’ question and the difference between the machine-readable triple and the human-readable graph in the next instalment.