Why rdf

This allows multiple schemas to be applied, interlinked, queried as one and modified without changing the data instances. In plain English, an RDF statement states facts, relationships and data by linking resources of different kinds. With the help of an RDF statement, just about anything can be expressed by a uniform structure, consisting of three linked data pieces.

This is how the RDF model triples the power of any given data piece by giving it the means to enter endless relationships with other data pieces and become the building block of greater, more flexible and richly interconnected data structures. It is important to know that all data, regardless of its format, can be converted to RDF data. Being a powerful and expressive framework for representing data, RDF is used for building knowledge graphs — richly interlinked, interoperable and flexible information structures.

Leverage semantics to satisfy a conceptual definition of data and enable you to address emerging use cases. Use data models to inform the data without enforcing structure on it, meaning the widest scope of interpretation and the smallest bias toward implementation strategy.

However, Semantic Web practitioners found it very difficult to deal with large amounts of triples for application development. There are lots of reasons that you would want to segment different subsets of triples from each other simplified access control, simplified updating, trust, etc. When referring to a triple in a named graph, you would often use 4-tuple notation instead of 3-tuple notation. The 4-tuple is of the form:.

For this reason, a triple store that supports named graphs is often called a quad store, though, somewhat confusingly, triple stores themselves are often quad stores anyway. That is, if an RDF database bills itself as a triple store it probably supports named graphs. This is by design and is a very important feature. By organizing the statement into named graphs, a Semantic Web application can implement access control, trust, data lineage, and other functionality very cleanly.

Exactly the best ways to segment triples in your application is an advanced topic that will be covered in future lessons, and is a large part of the value brought by Semantic Web platforms which will often hide the details and logic of named graph creation and segmentation to simplify application development.

This is a lot of information to cover in a single lesson, especially at this level of detail. However, it boils down to a very simple summary that will become second nature to you if you spend any time implementing Semantic Web technologies:. Contact Us Support. Toggle navigation. Semantic University. Introduction This set of lessons is an introduction to RDF, the core data model of the Semantic Web and the foundation of all other Semantic Web technologies.

In this lesson we will introduce RDF. There are three kinds of nodes in an RDF directed graph: Resource nodes. A resource is anything that can have things said about it.

In a visual representation, resources are represented by ovals. Literal nodes. The term literal is a fancy word for value.

In a visual representation, literals are represented by rectangles. Blank nodes. A blank node is a resource without a URI. You can think that the "description" RDF element gives the clue to the parser as to how to find the subjects, objects and verbs in what follows.

There are others which are longer, but more efficient when you have, for instance, sets of many properties of the same object. The useful thing is that of course they all convey the same triple. It is a mess when you use questions about a document to try to ask questions about what the document is trying to convey.

It will work. In a way. But flagging the grammar explicitly RDF syntax is a way of doing this is a whole lot better. I'll end this with some examples of the last problem. Using RDF makes things easier. If you haven't gone to the trouble of making a semantic model, then you may not have a well defined one. What does that mean?

I can give some general examples of ambiguities which crop up in practice. In RDF, you need a good idea about what is being said about what, and they would tend not to arise. Look at a label on the jam jar which says: "Expires ". What expires: the label, or the jam? Here the ambiguity is between a statement about a statement about a document, and a statement about a document.

Another example is an element which qualifies another apparently element. When information is assembled in a set of independently thrown in records often ambiguities can arise because of the lack of logic. HTTP headers or email headers are a good example. These things can work when one program handles all the records, but when you start mixing records you get trouble.

In XML it is all too easy to fall into the trap of having two elements, one describing the author, and a separate one as a flag that the "author" element in fact means not the direct author but that of a work translated to make the book in question. Therefore, a set of RDF statements can be translated into a graph of linked resources, and vice versa.

Also, with RDF you now have resources that are extremely flexible since modifications consist of adding or removing RDF triples. If Paul moved from San Francisco you would only need to remove that statement, deleting the link between the two resources without affecting the rest of your graph. Forget about dealing with static schemas and complex table modifications.

This is only one example of multiple specialized models that are public and available online. Finally, use data from different sources to empower your own data.