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joeflack4 / rdftab.rs

RDF Tables in Rust

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rdftab.rs: RDF Tables with Rust

rdftab reads RDFXML and generates a statements table like this:

stanza subject predicate object value datatype language
ex:foo ex:foo rdfs:label Foo
ex:foo ex:foo rdfs:label Fou fr
ex:foo ex:foo ex:size 123 xsd:int
ex:foo ex:foo ex:link http://example.com/foo
ex:foo ex:foo rdf:type owl:Class
ex:foo ex:foo rdfs:subClassOf _:b1
ex:foo _:b1 rdf:type owl:Restriction
ex:foo _:b1 owl:onProperty ex:part-of
ex:foo _:b1 owl:someValuesFrom ex:bar

This is an early prototype that only works with RDFXML input and SQLite databases. We use the Rust programming language to read and insert as quickly as possible, using as little memory as possible.

Usage

  1. download the binary for your platform from the "Assets" section of the latest release on the Releases page.
  2. make sure that the binary is executable
  3. create a SQLite database file with a prefix table
  4. run rdftab with the database you want to use, and the RDFXML input as STDIN
  5. query your database with SQLite
$ curl -L -o rdftab https://github.com/ontodev/rdftab.rs/releases/download/v0.1.1/rdftab-x86_64-apple-darwin
$ chmod +x rdftab
$ sqlite3 example.db < test/prefix.sql
$ ./rdftab example.db < test/example.owl
$ sqlite3 example.db
> select * from statements limit 3;

If macOS complains about running untrusted code (due to Gatekeeper code signing, see allow permissions), try sudo spctl --add rdftab.

Build

If we haven't provided a binary for your platform, or you want to modify the rdftab code, you can build the code as you would any Rust project:

  1. install Rust tools: rustup
  2. clone this repository: git clone https://github.com/ontodev/rdftab.rs && cd rdftab.rs
  3. run cargo build

Motivation

RDF data consists of subject-predicate-object triples that form a graph. With SPARQL we can perform complex queries over that graph. With OWLAPI we can interpret that graph as a rich set of logical axioms. But loading a large RDF graph into OWLAPI or a triplestore for SPARQL can be slow and require a lot of memory.

In many cases the queries we want to run are actually quite simple. We often just want all the triples associated with a set of terms, or all the subjects that match a given predicate and object. In these cases, SQLite is actually very fast, efficient, and effective. Better yet, you can use SQLite from the command line or pretty much any programming language.

Examples

Task SQL SPARQL
Get subjects with labels 
SELECT subject, value AS label
FROM statements
WHERE predicate = "rdfs:label";
 
SELECT ?subject, ?label
WHERE {
  ?subject rdfs:label ?label .
}
Get OWL classes with labels 
SELECT s1.subject, s2.value AS label
FROM statements s1
JOIN statements s2 ON s2.subject = s1.subject
WHERE s1.predicate = "rdf:type"
  AND s1.object = "owl:Class"
  AND s2.predicate = "rdfs:label";
 
SELECT ?subject, ?label
WHERE {
  ?subject
    rdf:type owl:Class ;
    rdfs:label ?label .
}
Get all triples for a subject, including nested anonymous structures such as OWL class expressions and OWL annotation axioms 
SELECT *
FROM statements
WHERE stanza = "ex:foo";
 Annoying...

Design

If you've worked with RDF before, all of these columns in the example above should be familiar, except for stanza. We'll discuss stanzas in a moment.

In each of these columns, values are encoded pretty much as you would in Turtle syntax:

  • IRIs (URLs) are wrapped in angle brackets: <http://example.com/foo>
  • prefixed names use a prefix from the prefix table: ex:foo
  • blank nodes start with _:: _:b1234

Some differences from Turtle syntax:

  • literals are multiline strings, without enclosing quotations marks or escaping
  • language tags do not include an @

This means it's quite simple to convert this table to Turtle format. As a first pass:

SELECT
  "@prefix " || prefix || ": <" || base || "> ."
FROM prefix
UNION ALL
SELECT 
   subject
|| " "
|| predicate
|| " "
|| coalesce(
     object,
     """" || value || """^^" || datatype,
     """" || value || """@" || language,
     """" || value || """"
   )
|| " ."
FROM statements;

The src/turtle.sql file is a more complete example, with better escaping of special characters.

Objects

We use four columns to encode RDF objects, which fall into four types:

  1. IRI: use the object column; value, datatype, and language are NULL
  2. Plain literal: use the value column; object, datatype, and language are NULL
  3. Typed literal: use the value and datatype columns; object and language are NULL
  4. Langage tagged literal: use the value and language columns; object and datatype are NULL

Prefixes

While any IRI can be wrapped in angle brackets, it's much easier for people to read prefixed names. When reading RDFXML rdftab uses a prefix table from your SQLite database, and tries to convert each IRI it encounters into a prefixes name. src/prefix.sql provides an example.

Some warnings:

  • Since SQL simply compares strings, not expanded IRIs, it's your job to ensure that your prefixes are consistent across your data.
  • Turtle prefixed names are a superset of XML QNames and a subset of CURIEs. rdftab's prefix handling is currently very primitive. Depending on your choices of prefixes and the IRIs in your RDF, rdftab may generate prefixed names that are not valid in Turtle.

Stanzas

The RDF graph structure is exceedingly simple. To encode data with more structure than a simple triple, we usually construct some sort of tree using blank nodes as subjects. To encode an OWL class expression "rdfs:subClassOf (ex:part-of some ex:bar)" we use a little tree like this:

ex:foo rdfs:subClassOf _:b1 .
_:b1 rdf:type owl:Restriction .
_:b1 owl:onProperty ex:part-of .
_:b1 owl:someValuesFrom ex:bar .

When we want to query for all the information about ex:foo, we can't simply ask for all the subjects matching ex:foo. We also have to query for _:b1. In general, we have to recurse through these trees of blank nodes.

Turtle provides some "syntactic sugar" for nested anonymous structures, and Turtle processors also group together all the triples about a given subject:

ex:foo
  rdfs:label "Foo", "Fou"@fr ;
  ex:size "123"^^xsd:int ;
  ex:link <http://example.com/foo> ;
  rdf:type owl:Class ;
  rdfs:subClassOf [
    rdf:type owl:Restriction ;
    owl:onProperty ex:part-of ;
    owl:someValuesFrom ex:bar
  ] .

When we ask for all the information about ex:foo this is what we want! In the Turtle grammar this is just called triples, but we call it a "stanza". RDFXML has a similar stanza structure, where each child element of the root element is a tree specifying a particular subject, and various nested anonymous structures are encoded in the XML tree structure.

<owl:Class rdf:about="http://example.com/foo">
  <rdfs:label>Foo</rdfs:label>
  <rdfs:label xml:lang="fr">Fou</rdfs:label>
  <ex:size rdf:datatype="http://www.w3.org/2001/XMLSchema#int">123</ex:size>
  <ex:link rdf:resource="http://example.com/foo"/>
  <rdfs:subClassOf>
    <owl:Restriction>
      <owl:onProperty rdf:resource="http://example.com/part-of"/>
      <owl:someValuesFrom rdf:resource="http://example.com/bar"/>
    </owl:Restriction>
  </rdfs:subClassOf>
</owl:Class>

(See example.owl.)

To encode stanza information in the statements table, rdftab uses a slightly modified version of rio that emits a special triple when a child element of the RDFXML root element is closed. This information is used to associate the "top-level subject" with all the triples that came out of that element. We put that top-level subject in the stanza column.

Looking back to our main example, you can see that the subjects ex:foo and _:b1 both the same stanza ex:foo. Now when we query SQLite for stanza = "ex:foo" we will get all the triples for the subject ex:foo and all of the nested anonymous structures.

Note that the stanza column is usually a named subject, but there are also cases where the top-level subject is a blank node.

OWL Annotation Axioms

OWL Annotation Axioms provide a way to make statements about other statements in the RDF graph. For example, we can add a comment on a label:

ex:foo rdfs:label "Foo" .
[ rdf:type owl:Axiom ;
  owl:annotatedSource ex:foo ;
  owl:annotatedProperty ex:label ;
  owl:annotatedTarget "Foo" ;
  rdfs:comment "A silly label"
] .

The top-level subject for the OWL Annotation Axiom is a blank node. However when we query for ex:foo we want to get this information as well. So rdftab looks for the owl:annotatedSource predicate, and uses the object of that triple as the stanza.

stanza subject predicate object value datatype language
ex:foo ex:foo rdfs:label Foo
ex:foo _:b1 rdf:type owl:Axiom
ex:foo _:b1 owl:annotatedSubject ex:foo
ex:foo _:b1 owl:annotatedProperty rdfs:label
ex:foo _:b1 owl:annotatedTarget Foo
ex:foo _:b1 rdfs:comment A silly label

Stanza edge cases for OWL

Note that OWL does not prioritize the directionality of some symmetric axiom types - for example, when you have a disjointness axiom or equivalence axiom connecting two named classes. In this case, the stanza is chosen arbitrarily.

E.g. in an ontology that has A disjointWith B we get:

stanza subject predicate object value datatype language
ex:B ex:B rdfs:label B
ex:B ex:B owl:disjointWith ex:a
ex:B ex:B rdf:type owl:Class
ex:a ex:a rdfs:label A
ex:a ex:a rdf:type owl:Class

This means that the convenience query shown above does not reliably fetch all axioms for a class. For example, in the above querying on A would not get the disjointness axiom

Additionally, the stanza name may not be meaningful for GCIs. For example, given an axiom in Manchester syntax:

develops-from some (part-of some B) DisjointWith develops-from some (part-of some A)

which gives you this RDF/OWL:

   <owl:Restriction>
        <owl:onProperty rdf:resource="http://example.com/develops-from"/>
        <owl:someValuesFrom>
            <owl:Restriction>
                <owl:onProperty rdf:resource="http://example.com/part-of"/>
                <owl:someValuesFrom rdf:resource="http://example.com/B"/>
            </owl:Restriction>
        </owl:someValuesFrom>
        <owl:disjointWith>
            <owl:Restriction>
                <owl:onProperty rdf:resource="http://example.com/develops-from"/>
                <owl:someValuesFrom>
                    <owl:Restriction>
                        <owl:onProperty rdf:resource="http://example.com/part-of"/>
                        <owl:someValuesFrom rdf:resource="http://example.com/a"/>
                    </owl:Restriction>
                </owl:someValuesFrom>
            </owl:Restriction>
        </owl:disjointWith>
    </owl:Restriction>

with RDFTab we get:

stanza subject predicate object value datatype language
owl:disjointWith _:riog00000011 owl:disjointWith _:riog00000013
owl:disjointWith _:riog00000013 owl:someValuesFrom _:riog00000014
owl:disjointWith _:riog00000014 owl:someValuesFrom ex:a
owl:disjointWith _:riog00000014 owl:onProperty ex:part-of
owl:disjointWith _:riog00000014 rdf:type owl:Restriction
owl:disjointWith _:riog00000013 owl:onProperty ex:develops-from
owl:disjointWith _:riog00000013 rdf:type owl:Restriction
owl:disjointWith _:riog00000011 owl:someValuesFrom _:riog00000012
owl:disjointWith _:riog00000012 owl:someValuesFrom ex:B
owl:disjointWith _:riog00000012 owl:onProperty ex:part-of
owl:disjointWith _:riog00000012 rdf:type owl:Restriction
owl:disjointWith _:riog00000011 owl:onProperty ex:develops-from
owl:disjointWith _:riog00000011 rdf:type owl:Restriction

In this case, to fetch all axioms for class ex:a or ex:B we need to iteratively query to walk up the graph.

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RDF Tables in Rust

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