Python library to serialize, parse and process RDF data
RDFLib is a Python library for working with RDF, a simple yet powerful language for representing information. This library contains parsers/serializers for almost all of the known RDF serializations, such as RDF/XML, Turtle, N-Triples, &amp; JSON-LD, many of which are now supported in their updated form (e.g. Turtle 1.1). The library also contains both in-memory and persistent Graph back-ends for storing RDF information and numerous convenience functions for declaring graph namespaces, lodging SPARQL queries and so on. It is in continuous development with the most recent stable release, rdflib 6.1.1 having been released on 20 December 2021. It was originally created by Daniel Krech with the first release in November, 2002.
A number of other Python projects use rdflib for RDF manipulation, including:
Overview.
RDFLib and Python idioms.
RDFLib's use of various Python idioms mean it is fairly simple for programmers with only junior Python skills to manipulate RDF. On the other hand, the Python idioms are simple enough that someone familiar with RDF, but not Python, can probably work out how to use rdflib quite easily.
The core class in RDFLib is Graph which is a Python dictionary used to store collections of RDF triples in memory. It redefines certain built-in Python object methods in order to exhibit simple graph behaviour, such as simple graph merging via addition (i.e. codice_1).
RDFLib graphs emulate container types and are best thought of as a set of 3-item triples:
 set([
 (subject,predicate,object),
 (subject1,predicate1,object1),
 (subjectN,predicateN,objectN)
RDFLib graphs are not sorted containers; they have ordinary Python set operations, e.g. codice_2 methods that search triples and return them in arbitrary order.
RDF graph terms.
The following RDFLib classes (listed below) model RDF terms in a graph and inherit from a common Identifier class, which extends Python unicode. Instances of these are nodes in an RDF graph.
Namespace utilities.
RDFLib provides mechanisms for managing namespaces. In particular, there is a Namespace class which takes (as its only argument) the Base URI of the namespace. Fully qualified URIs in the namespace can be constructed by attribute / dictionary access on Namespace instances:
» ; from rdflib import Namespace
» ; SDO = Namespace("[URL]
» ; SDO.Person
[URL]
» ; SDO['url']
[URL]
Graphs as iterators.
RDFLib graphs also override __"iter"__ in order to support iteration over the contained triples:
for subject, predicate, object_ in someGraph:
 assert (subject, predicate, object_) in someGraph, "Iterator / Container Protocols are Broken!!"
Set operations on RDFLib graphs.
__"iadd"__ and __"isub"__ are overridden to support adding and subtracting Graphs to/from each other (in place):
Basic triple matching.
RDFLib graphs support basic triple pattern matching with a "triples"((subject,predicate,object)) function. This function is a generator of triples that match the pattern given by the arguments. The arguments of these are RDF terms that restrict the triples that are returned. Terms that are "None" are treated as a wildcard.
for subject, predicate, object_ in someGraph.triples((None, URIRef("[URL] None)):
 print("{} has name {}".format(s, o)) # prints all the triples with the predicate being [URL]
Managing triples.
Adding triples.
Triples can be added in two ways:
Removing triples.
Similarly, triples can be removed by a call to "remove": "remove"((subject, predicate, object))
RDF Literal support.
RDFLib 'Literal's essentially behave like Unicode characters with an XML Schema datatype or language attribute. The class provides a mechanism to both convert Python literals (and their built-ins such as time/date/datetime) into equivalent RDF Literals and (conversely) convert Literals to their Python equivalent. There is some support of considering datatypes in comparing Literal instances, implemented as an override to __"eq"__. This mapping to and from Python literals is achieved with the following dictionaries:
PythonToXSD = {
 basestring : (None, None),
 float : (None, XSD_NS+u"float"),
 int : (None, XSD_NS+u"int"),
 long : (None, XSD_NS+u"long"), 
 bool : (None, XSD_NS+u"boolean"),
 date : (lambda i:i.isoformat(), XSD_NS+u"date"),
 time : (lambda i:i.isoformat(), XSD_NS+u"time"),
 datetime : (lambda i:i.isoformat(), XSD_NS+u"dateTime"),
Maps Python instances to WXS datatyped Literals
XSDToPython = { 
 XSD_NS+u"time" : (None, _strToTime),
 XSD_NS+u"date" : (None, _strToDate),
 XSD_NS+u"dateTime" : (None, _strToDateTime), 
 XSD_NS+u"string" : (None, None),
 XSD_NS+u"normalizedString" : (None, None),
 XSD_NS+u"token" : (None, None),
 XSD_NS+u"language" : (None, None),
 XSD_NS+u"boolean" : (None, lambda i:i.lower() in ["1", "true"]),
 XSD_NS+u"decimal" : (float, None),
 XSD_NS+u"integer" : (long, None),
 XSD_NS+u"nonPositiveInteger" : (int, None),
 XSD_NS+u"long" : (long, None),
 XSD_NS+u"nonNegativeInteger" : (int, None),
 XSD_NS+u"negativeInteger" : (int, None),
 XSD_NS+u"int" : (int, None),
 XSD_NS+u"unsignedLong" : (long, None),
 XSD_NS+u"positiveInteger" : (int, None),
 XSD_NS+u"short" : (int, None),
 XSD_NS+u"unsignedInt" : (long, None),
 XSD_NS+u"byte" : (int, None),
 XSD_NS+u"unsignedShort" : (int, None),
 XSD_NS+u"unsignedByte" : (int, None),
 XSD_NS+u"float" : (float, None),
 XSD_NS+u"double" : (float, None),
 XSD_NS+u"base64Binary" : (base64.decodestring, None),
 XSD_NS+u"anyURI" : (None,None),
Maps WXS datatyped Literals to Python. This mapping is used by the "toPython"() method defined on all Literal instances.
SPARQL querying.
RDFLIb supports a majority of the current SPARQL 1.1 specification and includes a harness for the publicly available RDF DAWG test suite. Support for SPARQL is provided by two methods:
Serialization (NTriples, N3, and RDF/XML).
The RDF store API.
A Universal RDF Store Interface
This document attempts to summarize some fundamental components of an RDF store. The motivation is to outline a standard set of interfaces for providing the necessary support needed in order to persist an RDF Graph in a way that is universal and not tied to any specific implementation. For the most part, the core RDF model is adhered to as well as terminology that is consistent with the RDF Model specifications. However, this suggested interface also extends an RDF store with additional requirements necessary to facilitate the aspects of Notation 3 that go beyond the RDF model to provide a framework for First Order Predicate Logic processing and persistence.
It's worth noting that the concept of logically grouping triples within an addressable 'set' or 'subgraph' is just barely beyond the scope of the RDF model. The RDF model defines a graph as an arbitrary collection of triples and the semantics of these triples, but doesn't give guidance on how to consistently address such arbitrary collections. Though a collection of triples can be thought of as a resource itself, the association between a triple and the collection it is a part of is not covered.
There are at least two situations where the closed world assumption is used. The first is where it is assumed that a knowledge base contains all relevant facts. This is common in corporate databases. That is, the information it contains is assumed to be complete
From a store perspective, closed world assumptions also provide the benefit of better query response times due to the explicit closed world boundaries. Closed world boundaries can be made transparent by federated queries that assume each ConjunctiveGraph is a section of a larger, unbounded universe. So a closed world assumption does not preclude you from an open-world assumption.
For the sake of persistence, Conjunctive Graphs must be distinguished by identifiers (that may not necessarily be RDF identifiers or may be an RDF identifier normalized - SHA1/MD5 perhaps - for database naming purposes ) which could be referenced to indicate conjunctive queries (queries made across the entire conjunctive graph) or appear as nodes in asserted statements. In this latter case, such statements could be interpreted as being made about the entire 'known' universe. For example:
Terminology.
 ;urn:uuid:conjunctive-graph-foo ; rdf:type :ConjunctiveGraph
 ;urn:uuid:conjunctive-graph-foo ; rdf:type log:Truth
 ;urn:uuid:conjunctive-graph-foo ; :persistedBy :MySQL
 ;templatestyles src="Template:Blockquote/styles.css" / ;Chimezie said "higher-order statements are complicated"
Which can be written as (in N3):
chimezie :said {:higherOrderStatements rdf:type :complicated}
Context quoting can be thought of as very similar to reification. The main difference is that quoted statements are not asserted or considered as statements of truth about the universe and can be referenced as a group: a hypothetical RDF Graph
Universal Quantifiers / Variables. (relevant references):
* OWL Definition of SWRL. (browse)
* SWRL/RuleML Variable
* Blank Nodes
* URI References
* Literals (which consist of a literal value, datatype and language tag)
Those that extend the RDF model into N3:
* Formulae
* Universal Quantifications (Variables)
And those that are primarily for matching against 'Nodes' in the underlying Graph:
* REGEX Expressions
* Date Ranges
* Numerical Ranges
"The set of nodes of an RDF graph is the set of subjects and objects of triples in the graph.
Such a store is responsible for maintaining this separation and ensuring that queries against the entire model (the aggregation of all the contexts - specified by not limiting a 'query' to a specifically name context) do not include quoted statements. Also, it is responsible for distinguishing universal quantifiers (variables).
These 2 additional concepts (formulae and variables) must be thought of as core extensions and distinguishable from the other terms of a triple (for the sake of the persistence roundtrip - at the very least). It's worth noting that the 'scope' of universal quantifiers (variables) and existential quantifiers (BNodes) is the formula (or context - to be specific) in which their statements reside. Beyond this, a Formula-aware store behaves the same as a Context-aware store.
Interpreting syntax.
The following Notation 3 document:
Could cause the following statements to be asserted in the store:
_:a log:implies _:b
This statement would be asserted in the partition associated with quoted statements (in a formula named _:a)
?x rdf:type :N3Programmer
Finally, these statements would be asserted in the same partition (in a formula named _:b)
?x :has _:c
_:c rdf:type :Migraine
Formulae and Variables as Terms
Formulae and variables are distinguishable from URI references, Literals, and BNodes by the following syntax:
 ?x - Variable
They must also be distinguishable in persistence to ensure they can be round tripped. Other issues regarding the persistence of N3 terms.
Database management.
An RDF store should provide standard interfaces for the management of database connections. Such interfaces are standard to most database management systems (Oracle, MySQL, Berkeley DB, Postgres, etc..)
The following methods are defined to provide this capability:
The configuration string is understood by the store implementation and represents all the necessary parameters needed to locate an individual instance of a store. This could be similar to an ODBC string, or in fact be an ODBC string if the connection protocol to the underlying database is ODBC. The open function needs to fail intelligently in order to clearly express that a store (identified by the given configuration string) already exists or that there is no store (at the location specified by the configuration string) depending on the value of create.
Triple interfaces.
An RDF store could provide a standard set of interfaces for the manipulation, management, and/or retrieval of its contained triples (asserted or quoted):
This function can be thought of as the primary mechanism for producing triples with nodes that match the corresponding terms and term pattern provided.
A conjunctive query can be indicated by either providing a value of NULL/None/Empty string value for context or the identifier associated with the Conjunctive Graph.
Formula / context interfaces.
These interfaces work on contexts and formulae (for stores that are formula-aware) interchangeably.
Named graphs / conjunctive graphs.
RDFLib defines the following kinds of Graphs:
A Conjunctive Graph is the most relevant collection of graphs that are considered to be the boundary for closed world assumptions. This boundary is equivalent to that of the store instance (which is itself uniquely identified and distinct from other instances of Store that signify other Conjunctive Graphs). It is equivalent to all the named graphs within it and associated with a _default_ graph which is automatically assigned a BNode for an identifier - if one isn't given.
Formulae.
RDFLib graphs support an additional extension of RDF semantics for formulae. For the academically inclined, Graham Klyne's 'formal' extension (see external links) is probably a good read.
Formulae are represented formally by the 'QuotedGraph' class and disjoint from regular RDF graphs in that their statements are quoted.
Persistence.
RDFLib provides an abstracted Store API for persistence of RDF and Notation 3. The Graph class works with instances of this API (as the first argument to its constructor) for triple-based management of an RDF store including: garbage collection, transaction management, update, pattern matching, removal, length, and database management (_"open"_ / _"close"_ / _"destroy"_) . Additional persistence mechanisms can be supported by implementing this API for a different store. Currently supported databases:
Store instances can be created with the "plugin" function:
from rdflib import plugin
from rdflib.store import Store
plugin.get(".. one of the supported Stores ..", Store)(identifier=.. id of conjunctive graph ..)
'Higher-order' idioms.
There are a few high-level APIs that extend RDFLib graphs into other Pythonic idioms. For more a more explicit Python binding, there are Sparta, SuRF &amp; FunOWL.
References.
 ;templatestyles src="Reflist/styles.css" / ;