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"""Utility methods for lexical algorithms""" |
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import collections |
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from rdflib import URIRef |
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from rdflib.namespace import SKOS |
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import numpy as np |
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from scipy.sparse import lil_matrix, csc_matrix |
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def get_subject_labels(graph, uri, properties, language): |
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return [str(label) |
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for prop in properties |
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for label in graph.objects(URIRef(uri), prop) |
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if label.language == language] |
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def make_relation_matrix(graph, vocab, property): |
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n_subj = len(vocab.subjects) |
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matrix = lil_matrix((n_subj, n_subj), dtype=np.bool) |
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for subj, obj in graph.subject_objects(property): |
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subj_id = vocab.subjects.by_uri(str(subj), warnings=False) |
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obj_id = vocab.subjects.by_uri(str(obj), warnings=False) |
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if subj_id is not None and obj_id is not None: |
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matrix[subj_id, obj_id] = True |
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return csc_matrix(matrix) |
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def make_collection_matrix(graph, vocab): |
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# make an index with all collection members |
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c_members = collections.defaultdict(list) |
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for coll, member in graph.subject_objects(SKOS.member): |
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member_id = vocab.subjects.by_uri(str(member), warnings=False) |
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if member_id is not None: |
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c_members[str(coll)].append(member_id) |
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c_matrix = lil_matrix((len(c_members), len(vocab.subjects)), |
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dtype=np.bool) |
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# populate the matrix for collection -> subject_id |
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for c_id, members in enumerate(c_members.values()): |
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c_matrix[c_id, members] = True |
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return csc_matrix(c_matrix) |
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NatLibFi /
Annif
