RDF data structures

RDF.ex provides various data structures for collections of statements:

  • RDF.Description: a collection of triples about the same subject
  • RDF.Graph: a named collection of statements
  • RDF.Dataset: a named collection of graphs, i.e. a collection of statements from different graphs; it may have multiple named graphs and at most one unnamed ("default") graph

All of these structures have similar sets of functions and implement Elixirs Enumerable and Collectable protocol, Elixirs Access behaviour and the RDF.Data protocol of RDF.ex.

The new function of these data structures create new instances of the struct and optionally initialize them with initial statements. RDF.Description.new requires at least an IRI or blank node for the subject, while RDF.Graph.new and RDF.Dataset.new take an optional IRI for the name of the graph or dataset via the name option.

empty_description = RDF.Description.new(EX.Subject)

empty_unnamed_graph = RDF.Graph.new
empty_named_graph   = RDF.Graph.new(name: EX.Graph)

empty_unnamed_dataset = RDF.Dataset.new
empty_named_dataset   = RDF.Dataset.new(name: EX.Dataset)

As you can see, qualified terms from a vocabulary namespace can be given instead of an IRI and will be resolved automatically. This applies to all of the functions discussed below.

The new functions can be called more shortly with the resp. delegator functions RDF.description, RDF.graph and RDF.dataset.

The new functions also take optional initial data, which can be provided in various forms. Basically it takes the given data and hands it to the add function with the newly created struct.

Adding statements

So let's look at these various forms of data the add function can handle.

Firstly, they can handle single statements:

description |> RDF.Description.add {EX.S, EX.p, EX.O}
graph       |> RDF.Graph.add {EX.S, EX.p, EX.O}
dataset     |> RDF.Dataset.add {EX.S, EX.p, EX.O, EX.Graph}

When the subject of a statement doesn't match the subject of the description, RDF.Description.add ignores it and is a no-op.

RDF.Description.add also accepts a property-value pair as a tuple.

RDF.Description.new(EX.S, {EX.p, EX.O1})
|> RDF.Description.add {EX.p, EX.O2}

In general, the object position of a statement can be a list of values, which will be interpreted as multiple statements with the same subject and predicate. So the former could be written more shortly:

RDF.Description.new(EX.S, {EX.p, [EX.O1, EX.O2]})

Multiple statements with different subject and/or predicate can be given as a list of statements, where everything said before on single statements applies to the individual statements of these lists:

description |> RDF.Description.add [{EX.p1, EX.O}, {EX.p2, [EX.O1, EX.O2]}
graph       |> RDF.Graph.add [{EX.S1, EX.p1, EX.o1}, {EX.S2, EX.p2, EX.o2}]
dataset     |> RDF.Dataset.add [{EX.S, EX.p, EX.o}, {EX.S, EX.p, EX.o, EX.Graph}

A RDF.Description can be added to any of the three data structures:

input = RDF.Description.new(EX.S, {EX.p, EX.O1})
description |> RDF.Description.add input
graph       |> RDF.Graph.add input
dataset     |> RDF.Dataset.add input

Note that, unlike mismatches in the subjects of directly given statements, RDF.Description.add ignores the subject of a given RDF.Description and just adds the property-value pairs of the given description, because this is a common use case when merging the descriptions of differently named resources (eg. because they are linked via owl:sameAs).

RDF.Graph.add and RDF.Dataset.add can also add other graphs and RDF.Dataset.add can add the contents of another dataset.

RDF.Dataset.add is also special, in that it allows to overwrite the explicit or implicit graph context of the input data and redirect the input into another graph. For example, the following examples all add the given statements to the EX.Other graph:

|> RDF.Dataset.add({EX.S, EX.p, EX.O}, EX.Other)
|> RDF.Dataset.add[{EX.S, EX.p, EX.O1, nil}, {EX.S, EX.p, EX.O2, EX.Graph}], EX.Other)
|> RDF.Dataset.add(RDF.Graph.new({EX.S, EX.p, EX.O3}, name: EX.Graph), EX.Other)

Unlike the add function, which always returns the same data structure as the data structure to which the addition happens, which possible means ignoring some input statements (eg. when the subject of a statement doesn't match the description subject) or reinterpreting some parts of the input statement (eg. ignoring the subject of another description), the merge function of the RDF.Data protocol implemented by all three data structures will always add all of the input and possibly creates another type of data structure. For example, merging two RDF.Descriptions with different subjects results in a RDF.Graph. Or adding a quad to a RDF.Graph with a different name than the quad’s graph context results in a RDF.Dataset.

RDF.Description.new(EX.S1, {EX.p, EX.O}) 
|> RDF.Data.merge(RDF.Description.new(EX.S2, {EX.p, EX.O})) # returns an unnamed RDF.Graph
|> RDF.Data.merge(RDF.Graph.new({EX.S2, EX.p, EX.O2}, name: EX.Graph)) # returns a RDF.Dataset

Statements added with put overwrite all existing statements with the same subject and predicate.

iex> RDF.Graph.new({EX.S1, EX.p, EX.O1}) |> RDF.Graph.put({EX.S1, EX.p, EX.O2})
#RDF.Graph{name: nil

It is available on all three data structures and can handle all of the input data types as their add counterpart.

Accessing the content of RDF data structures

All three RDF data structures implement the Enumerable protocol over the set of contained statements. As a set of triples in the case of RDF.Description and RDF.Graph and as a set of quads in case of RDF.Dataset. This means you can use all Enum functions over the contained statements as tuples.

RDF.Description.new(EX.S1, {EX.p, [EX.O1, EX.O2]})
|> Enum.each(&IO.inspect/1)

The RDF.Data protocol offers various functions to access the contents of RDF data structures:

  • RDF.Data.subjects/1 returns the set of all subject resources.
  • RDF.Data.predicates/1 returns the set of all used properties.
  • RDF.Data.objects/1 returns the set of all resources on the object position of statements. Note: Literals not included.
  • RDF.Data.resources/1 returns the set of all used resources at any position in the contained RDF statements.
  • RDF.Data.description/2 returns all statements from a data structure about the given resource as a RDF.Description. It will be empty if no such statements exist. On a RDF.Dataset it will aggregate the statements about the resource from all graphs.
  • RDF.Data.descriptions/1 returns all RDF.Descriptions within a data structure (possible aggregated in the case of a RDF.Dataset)
  • RDF.Data.statements/1 returns a list of all contained RDF statements.

The get functions return individual elements of a RDF data structure:

  • RDF.Description.get returns the list of all object values for a given property.
  • RDF.Graph.get returns the RDF.Description for a given subject resource.
  • RDF.Dataset.get returns the RDF.Graph with the given graph name.

All of these get functions return nil or the optionally given default value, when the given element can not be found.

iex> RDF.Description.new(EX.S1, {EX.p, [EX.O1, EX.O2]})
...> |> RDF.Description.get(EX.p)
[~I<http://example.com/O1>, ~I<http://example.com/O2>]

iex> RDF.Graph.new({EX.S1, EX.p, [EX.O1, EX.O2]})
...> |> RDF.Graph.get(EX.p2, :not_found)

You can get a single object value for a given predicate in a RDF.Description with the RDF.Description.first/2 function:

iex> RDF.Description.new(EX.S1, {EX.p, EX.O1})
...> |> RDF.Description.first(EX.p)

Since all three RDF data structures implement the Access behaviour, you can also use data[key] syntax, which basically just calls the resp. get function.

iex> description[EX.p]
[~I<http://example.com/O1>, ~I<http://example.com/O2>]

iex> graph[EX.p2] 

Also, the familiar fetch function of the Access behaviour, as a variant of get which returns ok tuples, is available on all RDF data structures.

iex> RDF.Description.new(EX.S1, {EX.p, [EX.O1, EX.O2]})
...> |> RDF.Description.fetch(EX.p)
{:ok, [~I<http://example.com/O1>, ~I<http://example.com/O2>]}

iex> RDF.Graph.new({EX.S1, EX.p, [EX.O1, EX.O2]})
...> |> RDF.Graph.fetch(EX.p2)

RDF.Dataset also provides the following functions to access individual graphs:

  • RDF.Dataset.graphs returns the list of all the graphs of the dataset
  • RDF.Dataset.default_graph returns the default graph of the dataset
  • RDF.Dataset.graph returns the graph of the dataset with the given name

Querying graphs with the SPARQL query language

The SPARQL.ex package allows you to execute SPARQL queries against RDF.ex data structures. It's still very limited at the moment. It just supports SELECT queries with basic graph pattern matching, filtering and projection and works on RDF.Graphs only. But even in this early, limited form it allows to express more powerful queries in a simpler way than with the plain RDF.Graph API.

See the SPARQL.ex guide for more information and some examples.

Deleting statements

Statements can be deleted in two slightly different ways. One way is to use the delete function of the resp. data structure. It accepts all the supported ways for specifying collections of statements supported by the resp. add counterparts and removes the found triples.

iex> RDF.Description.new(EX.S1, {EX.p, [EX.O1, EX.O2]})
...> |> RDF.Description.delete({EX.S1, EX.p, EX.O1})
#RDF.Description{subject: ~I<http://example.com/S1>

Another way to delete statements is the delete function of the RDF.Data protocol. The only difference to delete functions on the data structures directly is how it handles the deletion of a RDF.Description from another RDF.Description or RDF.Graph from another RDF.Graph. While the dedicated RDF data structure function ignores the description subject or graph name and removes the statements even when they don't match, RDF.Data.delete only deletes when the description’s subject resp. graph name matches.

iex> RDF.Description.new(EX.S1, {EX.p, [EX.O1, EX.O2]})
...> |> RDF.Description.delete(RDF.Description.new(EX.S2, {EX.p, EX.O1}))
#RDF.Description{subject: ~I<http://example.com/S1>

iex> RDF.Description.new(EX.S1, {EX.p, [EX.O1, EX.O2]})
...> |> RDF.Data.delete(RDF.Description.new(EX.S2, {EX.p, EX.O1}))
#RDF.Description{subject: ~I<http://example.com/S1>

Beyond that, there is

  • RDF.Description.delete_predicates which deletes all statements with the given property from a RDF.Description,
  • RDF.Graph.delete_subjects which deletes all statements with the given subject resource from a RDF.Graph,
  • RDF.Dataset.delete_graph which deletes all graphs with the given graph name from a RDF.Dataset and
  • RDF.Dataset.delete_default_graph which deletes the default graph of a RDF.Dataset.


RDF data structures can be compared for equality with the equal?/2 function of the respective data structure. You should these instead of comparisons with ==, because the data structures might contain fields which are not relevant for equality. For example the defined prefixes (see here for more on that) are ignored for this comparison.

iex> d = RDF.description(EX.S, EX.p, EX.O)
iex> RDF.Description.equal?(d, d)
iex> RDF.Graph.equal?(
...>   RDF.graph(d, prefixes: %{ex: EX}), 
...>   RDF.graph(d, prefixes: %{ex: EX, xsd: XSD}))
...> RDF.graph(d, prefixes: %{ex: EX}) ==
...>   RDF.graph(d, prefixes: %{ex: EX, xsd: XSD}))

You can also compare different types of RDF data structures with the RDF.Data.equal?/2 function, which takes just the raw data into account.

iex> RDF.Data.equal?(d, RDF.graph(d))

As opposed to RDF.Graph.equal?/2 the RDF.Data.equal?/2 function also doesn't consider the graph name when comparing RDF.Graphs.

iex> RDF.Graph.equal?(RDF.graph(d), RDF.graph(d, name: EX.Graph))
iex> RDF.Data.equal?(RDF.graph(d), RDF.graph(d, name: EX.Graph))