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Rdf Graph

This module provides functionality to convert video annotations into JSON-LD format. It includes the following main functions:

  1. annotations_to_jsonLD(annotations, isAutomatic: bool):

    • Converts video annotations into a JSON-LD graph.
    • Binds various namespaces to the RDF graph.
    • Adds nodes and triples for video, concepts, and prerequisites.
    • Serializes the RDF graph into JSON-LD format.
    • Compacts the JSON-LD using a provided context.
    • Returns the RDF graph and the compacted JSON-LD data.

  2. graph_to_rdf(jsonld):

    • Converts JSON-LD data into an RDF graph.

annotations_to_jsonLD(annotations, isAutomatic)

Converts video annotations into a JSON-LD graph.

Parameters:

Name Type Description Default
annotations dict

A dictionary containing video annotations.

 required
isAutomatic bool

A flag indicating whether the annotations are automatic.

 required

Returns:

Name Type Description
g Graph

The RDF graph containing the annotations.
data dict

The compacted JSON-LD data.

Examples:

>>> annotations = {
...     "id": "video1",
...     "definitions": [{"concept": "example", "description_type": "type", "start": "2023-01-01T00:00:00", "start_sent_id": "1", "end": "2023-01-01T00:01:00", "end_sent_id": "2"}],
...     "relations": [{"target": "example", "prerequisite": "example2", "time": "2023-01-01T00:00:00", "xywh": "None", "sent_id": "1", "word_id": "None"}],
...     "annotator": "http://example.org/annotator"
... }
>>> g, data = annotations_to_jsonLD(annotations, isAutomatic=False)
Source code in apps/annotator/code/ontology/rdf_graph.py 
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def annotations_to_jsonLD(annotations, isAutomatic:bool):
    """
    Converts video annotations into a JSON-LD graph.

    Parameters
    ----------
    annotations : dict
        A dictionary containing video annotations.
    isAutomatic : bool
        A flag indicating whether the annotations are automatic.

    Returns
    -------
    g : rdflib.Graph
        The RDF graph containing the annotations.
    data : dict
        The compacted JSON-LD data.

    Examples
    --------
    >>> annotations = {
    ...     "id": "video1",
    ...     "definitions": [{"concept": "example", "description_type": "type", "start": "2023-01-01T00:00:00", "start_sent_id": "1", "end": "2023-01-01T00:01:00", "end_sent_id": "2"}],
    ...     "relations": [{"target": "example", "prerequisite": "example2", "time": "2023-01-01T00:00:00", "xywh": "None", "sent_id": "1", "word_id": "None"}],
    ...     "annotator": "http://example.org/annotator"
    ... }
    >>> g, data = annotations_to_jsonLD(annotations, isAutomatic=False)
    """

    print("***** EKEEL - Video Annotation: ontology.py::to_jsonLD(): Inizio ******")

    concepts_anno = []
    prereq_anno = []

    if "definitions" in annotations:
        concepts_anno = annotations["definitions"]
    if "relations" in annotations:    
        prereq_anno = annotations["relations"]
    video_id = annotations["id"]

    if not isAutomatic:
        creator = annotations["annotator"]
        creator = URIRef(creator)

    g = Graph()

    g.bind("oa", oa)
    g.bind("dctypes", dctypes)
    g.bind("edu", edu)
    g.bind("SKOS", SKOS)
    g.bind("dcterms", dcterms)


    # aggiunge un nodo
    video = URIRef("video_" + str(video_id))
    #video = URIRef(edurell + "video_" + str(video_id))
    g.add((video, RDF.type, dctypes.MovingImage))

    conll = URIRef("conll_" + str(video_id))
    #conll = URIRef(edurell + "conll_" + str(video_id))
    g.add((conll, RDF.type, dctypes.Text))


    # collegamento video conll
    ann_linking_conll = URIRef("ann0")
    #ann_linking_conll = URIRef(edurell + "ann0")
    g.add((ann_linking_conll, RDF.type, oa.Annotation))
    g.add((ann_linking_conll, oa.motivatedBy, edu.linkingConll))
    g.add((ann_linking_conll, oa.hasBody, conll))
    g.add((ann_linking_conll, oa.hasTarget, video))

    date = Literal(datetime.now())

    #creo il nuovo nodo dei concetti
    #localVocabulary = URIRef("localVocabulary")
    #g.add((localVocabulary, RDF.type, SKOS.Collection))


    # per ogni annotazione di concetto spiegato aggiungo le triple
    for i, annotation in enumerate(concepts_anno):
        ann = URIRef("ann" + str(i + 1))

        g.add((ann, RDF.type, oa.Annotation))

        if isAutomatic:
            g.add((ann, dcterms.creator, URIRef(annotation["creator"])))
        else:
            g.add((ann, dcterms.creator, creator))

        g.add((ann, dcterms.created, date))
        g.add((ann, oa.motivatedBy, oa.describing))
        g.add((ann, SKOS.note, Literal("concept"+annotation["description_type"])))


        concept = URIRef("concept_" + annotation["concept"].replace(" ", "_"))

        #crea un nodo concetto

        #g.add((localVocabulary, SKOS.member, concept))
        #g.add((concept, RDF.type, SKOS.Concept))


        g.add((ann, oa.hasBody, concept))

        blank_target = BNode()


        blank_selector = BNode()

        g.add((ann, oa.hasTarget, blank_target))
        g.add((blank_target, RDF.type, oa.SpecificResource))

        g.add((blank_target, oa.hasSelector, blank_selector))
        g.add((blank_selector, RDF.type, oa.RangeSelector))

        g.add((blank_target, oa.hasSource, video))

        blank_startSelector = BNode()
        blank_endSelector = BNode()

        g.add((blank_startSelector, RDF.type, edu.InstantSelector))
        g.add((blank_endSelector, RDF.type, edu.InstantSelector))

        g.add((blank_selector, oa.hasStartSelector, blank_startSelector))
        g.add((blank_selector, oa.hasEndSelector, blank_endSelector))

        g.add((blank_startSelector, RDF.value, Literal(annotation["start"] + "^^xsd:dateTime")))
        g.add((blank_startSelector, edu.conllSentId, Literal(annotation["start_sent_id"])))
        #g.add((blank_startSelector, edu.conllWordId, Literal(annotation["word_id"])))

        g.add((blank_endSelector, RDF.value, Literal(annotation["end"] + "^^xsd:dateTime")))
        g.add((blank_endSelector, edu.conllSentId, Literal(annotation["end_sent_id"])))


    num_definitions = len(concepts_anno) + 1

    # per ogni annotazione di prerequisito aggiungo le triple
    for i, annotation in enumerate(prereq_anno):
        ann = URIRef("ann" + str(num_definitions + i))

        target_concept = URIRef("concept_" +  annotation["target"].replace(" ", "_"))
        prereq_concept = URIRef("concept_" +  annotation["prerequisite"].replace(" ", "_"))

        #g.add((target_concept, RDF.type, SKOS.Concept))
        #g.add((prereq_concept, RDF.type, SKOS.Concept))

        g.add((ann, RDF.type, oa.Annotation))

        if isAutomatic:
            g.add((ann, dcterms.creator, URIRef(annotation["creator"])))
        else:
            g.add((ann, dcterms.creator, creator))

        g.add((ann, dcterms.created, date))
        g.add((ann, oa.motivatedBy, edu.linkingPrerequisite))

        g.add((ann, oa.hasBody, prereq_concept))
        g.add((ann, SKOS.note, Literal(annotation["weight"] + "Prerequisite")))

        blank_target = BNode()

        g.add((ann, oa.hasTarget, blank_target))
        g.add((blank_target, RDF.type, oa.SpecificResource))
        g.add((blank_target, dcterms.subject, target_concept))

        g.add((blank_target, oa.hasSource, video))

        blank_selector_video = BNode()

        g.add((blank_target, oa.hasSelector, blank_selector_video))
        g.add((blank_selector_video, RDF.type, edu.InstantSelector))
        g.add((blank_selector_video, RDF.value, Literal(annotation["time"] + "^^xsd:dateTime")))

        if annotation["xywh"] != "None":
            g.add((blank_selector_video, edu.hasMediaFrag, Literal(annotation["xywh"])))


        g.add((blank_selector_video, edu.conllSentId, Literal(annotation["sent_id"])))

        if annotation["word_id"] != "None":
            g.add((blank_selector_video, edu.conllWordId, Literal(annotation["word_id"])))

    # stampo il grafo in formato turtle
    # turtle = g.serialize(format='turtle').decode("utf-8")
    # print(turtle)

    # creo file json-ld

    #jsonld = json.loads(g.serialize(format='json-ld', context=context))

    jsonld = json.loads(g.serialize(format='json-ld'))
    jsonld = pyld.jsonld.compact(jsonld, context)

    '''
    Nested nodes creation

    FROM:                            |  TO:
    {                                |  {
        ...                          |    ...
        "target": 123                |     "target":{
    },                               |                 "id":123,
    {                                |                  "type":example
        "id": 123,                   |               }
        "type": example              |   }
    }

    '''

    for o in jsonld["@graph"]:
        if "target" in o:
            for i, t in enumerate(jsonld["@graph"]):
                if o["motivation"] != "edu:linkingConll" and o["target"] == t["id"]:
                    o["target"] = t
                    del jsonld["@graph"][i]
                    for j, s in enumerate(jsonld["@graph"]):
                        if o["target"]["selector"] == s["id"]:
                            o["target"]["selector"] = s
                            del jsonld["@graph"][j]

                            if o["motivation"] == "describing":
                                for k, p in enumerate(jsonld["@graph"]):
                                    if o["target"]["selector"]["startSelector"] == p["id"]:
                                        o["target"]["selector"]["startSelector"] = p
                                        del jsonld["@graph"][k]
                                        break

                                for k, p in enumerate(jsonld["@graph"]):
                                    if o["target"]["selector"]["endSelector"] == p["id"]:
                                        o["target"]["selector"]["endSelector"] = p
                                        del jsonld["@graph"][k]
                                        break

    #print(jsonld)
    data = {
        "graph":jsonld
    }

    #print(data)
    print("***** EKEEL - Video Annotation: ontology.py::to_jsonLD(): Fine ******")

    return g, data

graph_to_rdf(jsonld)

Converts JSON-LD data into an RDF graph.

Parameters:

Name Type Description Default
jsonld dict

A dictionary containing JSON-LD data.

 required

Returns:

Name Type Description
g Graph

The RDF graph parsed from the JSON-LD data.

Examples:

>>> jsonld = {
...     "@context": "http://schema.org",
...     "@type": "Person",
...     "name": "Jane Doe"
... }
>>> g = graph_to_rdf(jsonld)
Source code in apps/annotator/code/ontology/rdf_graph.py 
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def graph_to_rdf(jsonld):
    """
    Converts JSON-LD data into an RDF graph.

    Parameters
    ----------
    jsonld : dict
        A dictionary containing JSON-LD data.

    Returns
    -------
    g : rdflib.Graph
        The RDF graph parsed from the JSON-LD data.

    Examples
    --------
    >>> jsonld = {
    ...     "@context": "http://schema.org",
    ...     "@type": "Person",
    ...     "name": "Jane Doe"
    ... }
    >>> g = graph_to_rdf(jsonld)
    """
    json_expanded = pyld.jsonld.expand(jsonld)

    return Graph().parse(data=json.dumps(json_expanded), format='json-ld')