Linked Data Fragments: a uniform view on all Linked Data interfaces

Today’s Web offers three common ways to access Linked Data:

Linked Data Fragments is a conceptual framework that provides a uniform view on all possible interfaces to RDF, by observing that each interface partitions a dataset into its own specific kind of fragments.
Linked Data Fragment (LDF) is characterized by a specific selector (subject URI, SPARQL query, …), metadata (variable names, counts, …), and controls (links or URIs to other fragments).

While the above options are the most common ways to offer datasets, we could think of many others.
Our goal is to explore those other ways, in order to optimize the balance between server and client effort.

The axis of Linked Data Fragments types

The Linked Data Fragments vision allows us to visualize different HTTP interfaces for Linked Data together.

high client cost high availability high bandwidth high server cost low availability low bandwidth data dump subject page Triple Pattern Fragment SPARQL query result

SPARQL endpoints are easy for clients, as they allow highly specific fragment selection. However, they also have the highest server cost which makes it expensive to host them with decent availability. If you don’t want to depend on such an endpoint, you download a data dump, but then you’re querying a local source instead of the Web. With subject pages, servers do minimal effort, but clients need to work hard to solve simple queries.

Can we minimize server resource usage while still enabling clients to query data sources efficiently?
Such solutions can be found along the above axis by defining new Linked Data Fragment types.

New fragment types enable high-availability querying at low cost

We develop fragment types that require minimal server effort and enable efficient client-side querying. One such type is called a Triple Pattern Fragment (or basic Linked Data Fragment). It consists of:

Servers that offer such fragments are called Triple Pattern Fragments servers.
Triple Pattern Fragments clients can solve many SPARQL queries efficiently.