Source code for rdflib_jsonld.parser

# -*- coding: utf-8 -*-
"""
This parser will interpret a JSON-LD document as an RDF Graph. See:

    http://json-ld.org/

Example usage::

    >>> from rdflib.plugin import register, Parser
    >>> register('json-ld', Parser, 'rdflib_jsonld.parser', 'JsonLDParser')

    >>> from rdflib import Graph, URIRef, Literal
    >>> test_json = '''
    ... {
    ...     "@context": {
    ...         "dcterms": "http://purl.org/dc/terms/",
    ...         "rdf": "http://www.w3.org/1999/02/22-rdf-syntax-ns#",
    ...         "rdfs": "http://www.w3.org/2000/01/rdf-schema#"
    ...     },
    ...     "@id": "http://example.org/about",
    ...     "dcterms:title": {
    ...         "@language": "en",
    ...         "@value": "Someone's Homepage"
    ...     }
    ... }
    ... '''
    >>> g = Graph().parse(data=test_json, format='json-ld')
    >>> list(g) == [(URIRef('http://example.org/about'),
    ...     URIRef('http://purl.org/dc/terms/title'),
    ...     Literal("Someone's Homepage", lang='en'))]
    True

"""
# NOTE: This code reads the entire JSON object into memory before parsing, but
# we should consider streaming the input to deal with arbitrarily large graphs.

import warnings
from rdflib.graph import ConjunctiveGraph
from rdflib.parser import Parser, URLInputSource
from rdflib.namespace import RDF, XSD
from rdflib.term import URIRef, BNode, Literal

from ._compat import basestring, unicode
from .context import Context, Term, UNDEF
from .util import source_to_json, VOCAB_DELIMS, context_from_urlinputsource
from .keys import CONTEXT, GRAPH, ID, INDEX, LANG, LIST, REV, SET, TYPE, VALUE, VOCAB

__all__ = ["JsonLDParser", "to_rdf"]


# Add jsonld suffix so RDFLib can guess format from file name
try:
    from rdflib.util import SUFFIX_FORMAT_MAP

    if "jsonld" not in SUFFIX_FORMAT_MAP:
        SUFFIX_FORMAT_MAP["jsonld"] = "application/ld+json"
except ImportError:
    pass


TYPE_TERM = Term(unicode(RDF.type), TYPE, VOCAB)

ALLOW_LISTS_OF_LISTS = True  # NOTE: Not allowed in JSON-LD 1.0


[docs]class JsonLDParser(Parser): def __init__(self): super(JsonLDParser, self).__init__() def parse(self, source, sink, normalize_literals=True, **kwargs): # TODO: docstring w. args and return value encoding = kwargs.get("encoding") or "utf-8" if encoding not in ("utf-8", "utf-16"): warnings.warn( "JSON should be encoded as unicode. " + "Given encoding was: %s" % encoding ) base = kwargs.get("base") or sink.absolutize( source.getPublicId() or source.getSystemId() or "" ) context_data = kwargs.get("context") if not context_data and isinstance(source, URLInputSource): context_data = context_from_urlinputsource(source) produce_generalized_rdf = kwargs.get("produce_generalized_rdf", False) data = source_to_json(source) # NOTE: A ConjunctiveGraph parses into a Graph sink, so no sink will be # context_aware. Keeping this check in case RDFLib is changed, or # someone passes something context_aware to this parser directly. if not sink.context_aware: conj_sink = ConjunctiveGraph(store=sink.store, identifier=sink.identifier) else: conj_sink = sink to_rdf( data, conj_sink, base, context_data, normalize_literals=normalize_literals )
[docs]def to_rdf( data, dataset, base=None, context_data=None, produce_generalized_rdf=False, allow_lists_of_lists=None, normalize_literals=True, ): # TODO: docstring w. args and return value context = Context(base=base) if context_data: context.load(context_data) parser = Parser( generalized_rdf=produce_generalized_rdf, allow_lists_of_lists=allow_lists_of_lists, ) return parser.parse(data, context, dataset, normalize_literals=normalize_literals)
class Parser(object): def __init__(self, generalized_rdf=False, allow_lists_of_lists=None): self.generalized_rdf = generalized_rdf self.allow_lists_of_lists = ( allow_lists_of_lists if allow_lists_of_lists is not None else ALLOW_LISTS_OF_LISTS ) def parse(self, data, context, dataset, normalize_literals=True): topcontext = False if isinstance(data, list): resources = data elif isinstance(data, dict): l_ctx = data.get(CONTEXT) if l_ctx: context.load(l_ctx, context.base) topcontext = True resources = data if not isinstance(resources, list): resources = [resources] if context.vocab: dataset.bind(None, context.vocab) for name, term in context.terms.items(): if term.id and term.id.endswith(VOCAB_DELIMS): dataset.bind(name, term.id) graph = dataset.default_context if dataset.context_aware else dataset for node in resources: self._add_to_graph( dataset, graph, context, node, topcontext, normalize_literals=normalize_literals, ) return graph def _add_to_graph( self, dataset, graph, context, node, topcontext=False, normalize_literals=True ): if not isinstance(node, dict) or context.get_value(node): return if CONTEXT in node and not topcontext: l_ctx = node.get(CONTEXT) if l_ctx: context = context.subcontext(l_ctx) else: context = Context(base=context.doc_base) id_val = context.get_id(node) if isinstance(id_val, basestring): subj = self._to_rdf_id(context, id_val) else: subj = BNode() if subj is None: return None # NOTE: crude way to signify that this node might represent a named graph no_id = id_val is None for key, obj in node.items(): if key in (CONTEXT, ID) or key in context.get_keys(ID): continue if key == REV or key in context.get_keys(REV): for rkey, robj in obj.items(): self._key_to_graph( dataset, graph, context, subj, rkey, robj, reverse=True, no_id=no_id, normalize_literals=normalize_literals, ) else: self._key_to_graph( dataset, graph, context, subj, key, obj, no_id=no_id, normalize_literals=normalize_literals, ) return subj def _key_to_graph( self, dataset, graph, context, subj, key, obj, reverse=False, no_id=False, normalize_literals=True, ): if isinstance(obj, list): obj_nodes = obj else: obj_nodes = [obj] term = context.terms.get(key) if term: term_id = term.id if term.container == LIST: obj_nodes = [{LIST: obj_nodes}] elif isinstance(obj, dict): if term.container == INDEX: obj_nodes = [] for values in obj.values(): if not isinstance(values, list): obj_nodes.append(values) else: obj_nodes += values elif term.container == LANG: obj_nodes = [] for lang, values in obj.items(): if not isinstance(values, list): values = [values] for v in values: obj_nodes.append((v, lang)) else: term_id = None if TYPE in (key, term_id): term = TYPE_TERM elif GRAPH in (key, term_id): if dataset.context_aware and not no_id: subgraph = dataset.get_context(subj) else: subgraph = graph for onode in obj_nodes: self._add_to_graph( dataset, subgraph, context, onode, normalize_literals=normalize_literals, ) return elif SET in (key, term_id): for onode in obj_nodes: self._add_to_graph( dataset, graph, context, onode, normalize_literals=normalize_literals, ) return pred_uri = term.id if term else context.expand(key) flattened = [] for obj in obj_nodes: if isinstance(obj, dict): objs = context.get_set(obj) if objs is not None: obj = objs if isinstance(obj, list): flattened += obj continue flattened.append(obj) obj_nodes = flattened if not pred_uri: return if term and term.reverse: reverse = not reverse bid = self._get_bnodeid(pred_uri) if bid: if not self.generalized_rdf: return pred = BNode(bid) else: pred = URIRef(pred_uri) for obj_node in obj_nodes: obj = self._to_object( dataset, graph, context, term, obj_node, normalize_literals=normalize_literals, ) if obj is None: continue if reverse: graph.add((obj, pred, subj)) else: graph.add((subj, pred, obj)) def _to_object( self, dataset, graph, context, term, node, inlist=False, normalize_literals=True ): if node is None: return if isinstance(node, tuple): value, lang = node if value is None: return return Literal(value, lang=lang) if isinstance(node, dict): node_list = context.get_list(node) if node_list is not None: if inlist and not self.allow_lists_of_lists: return listref = self._add_list(dataset, graph, context, term, node_list) if listref: return listref else: # expand.. if not term or not term.type: if isinstance(node, float): return Literal( node, datatype=XSD.double, normalize=normalize_literals ) if term and term.language is not UNDEF: lang = term.language else: lang = context.language return Literal(node, lang=lang) else: if term.type == ID: node = {ID: context.resolve(node)} elif term.type == VOCAB: node = {ID: context.expand(node) or context.resolve_iri(node)} else: node = {TYPE: term.type, VALUE: node} lang = context.get_language(node) if lang or context.get_key(VALUE) in node or VALUE in node: value = context.get_value(node) if value is None: return None datatype = not lang and context.get_type(node) or None if lang: return Literal(value, lang=lang) elif datatype: return Literal( value, datatype=context.expand(datatype), normalize=normalize_literals, ) else: return Literal(value) else: return self._add_to_graph(dataset, graph, context, node) def _to_rdf_id(self, context, id_val): bid = self._get_bnodeid(id_val) if bid: return BNode(bid) else: uri = context.resolve(id_val) if not self.generalized_rdf and ":" not in uri: return None return URIRef(uri) def _get_bnodeid(self, ref): if not ref.startswith("_:"): return bid = ref.split("_:", 1)[-1] return bid or None def _add_list(self, dataset, graph, context, term, node_list): if not isinstance(node_list, list): node_list = [node_list] first_subj = BNode() subj, rest = first_subj, None for node in node_list: if node is None: continue if rest: graph.add((subj, RDF.rest, rest)) subj = rest obj = self._to_object(dataset, graph, context, term, node, inlist=True) if obj is None: continue graph.add((subj, RDF.first, obj)) rest = BNode() if rest: graph.add((subj, RDF.rest, RDF.nil)) return first_subj else: return RDF.nil