Chapter 5. Data Management Patterns


While the statements in an RDF dataset describe a directed graph of connections between resources, the collection of triples itself has no structure: it is just a set of RDF statements. This lack of structure is not a problem for many simple RDF applications; the application code and behaviour is focused on exploring the connections in the graph. But for more complex systems that involve integrating data from many different sources it becomes useful to be able to partition a graph into a collection of smaller sub-graphs.

One reason for partitioning of the graph, is to support data extraction. Creating a useful view over one or more resources in a graph, e.g. to drive a user interface. There are a number of different partitioning mechanisms that can be used and these are covered in the Bounded Description pattern described in the next chapter.

A very important reason for wanting to partition a graph is to make data management simpler. By partitioning a graph according to its source or the kinds of statements it contains we can make it easier to organise and update a dataset. Managing smaller graphs gives more affordance to the data, allowing entire collections of statements to be manipulated more easily.

The means by which this affordance is created is by extending the core triple model of RDF to include an extra identifier. This allows us to identify collections of RDF triples, known as Named Graphs. The patterns captured in this chapter describe different approaches for managing RDF data using Named Graphs. The patterns cover different approaches for deciding on the scope of individual graphs, as well as how to annotate individual graphs, as well as ultimately re-assembling graphs back into a useful whole.

It should be apparent that Named Graphs is essentially a document-oriented approach to managing RDF data. Each document contains a collection of RDF statements. This means that we can benefit from thinking about good document design when determining the scope of each graph, as well as more general document management practices in deciding how to organise our data.

The beauty of the RDF model is that it is trivial to manage a triple store as a collection of documents (graphs) whilst still driving application logic from the overall web of connections described by the statements contained in those documents. An XML database might also offer facilities for managing collections of XML documents, but there is no standard way in which the content of those documents can be viewed or manipulated. In contrast the data merging model described by RDF provides a principled way to merge data across documents (Union Graph).

This flexibility provides some powerful data management options for RDF applications.

Table of Contents

Graph Annotation
Graph Per Aspect
Graph Per Resource
Graph Per Source
Named Graph
Union Graph