Datasets:
Tasks:
Text Classification
Languages:
English
Multilinguality:
monolingual
Size Categories:
10K<n<100K
Language Creators:
found
Annotations Creators:
expert-generated
File size: 17,310 Bytes
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# coding=utf-8
# Copyright 2022 The current dataset script contributor.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""The KnowledgeNet dataset for automatically populating a knowledge base"""
import json
import re
import datasets
_CITATION = """\
@inproceedings{mesquita-etal-2019-knowledgenet,
title = "{K}nowledge{N}et: A Benchmark Dataset for Knowledge Base Population",
author = "Mesquita, Filipe and
Cannaviccio, Matteo and
Schmidek, Jordan and
Mirza, Paramita and
Barbosa, Denilson",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
month = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-1069",
doi = "10.18653/v1/D19-1069",
pages = "749--758",}
"""
_DESCRIPTION = """\
KnowledgeNet is a benchmark dataset for the task of automatically populating a knowledge base (Wikidata) with facts
expressed in natural language text on the web. KnowledgeNet provides text exhaustively annotated with facts, thus
enabling the holistic end-to-end evaluation of knowledge base population systems as a whole, unlike previous benchmarks
that are more suitable for the evaluation of individual subcomponents (e.g., entity linking, relation extraction).
For instance, the dataset contains text expressing the fact (Gennaro Basile; RESIDENCE; Moravia), in the passage:
"Gennaro Basile was an Italian painter, born in Naples but active in the German-speaking countries. He settled at Brünn,
in Moravia, and lived about 1756..."
For a description of the dataset and baseline systems, please refer to their
[EMNLP paper](https://github.com/diffbot/knowledge-net/blob/master/knowledgenet-emnlp-cameraready.pdf).
Note: This Datasetreader currently only supports the `train` split and does not contain negative examples
"""
_HOMEPAGE = "https://github.com/diffbot/knowledge-net"
_LICENSE = ""
# The HuggingFace dataset library don't host the datasets but only point to the original files
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
_URLS = {
"train": "https://raw.githubusercontent.com/diffbot/knowledge-net/master/dataset/train.json",
"test": "https://raw.githubusercontent.com/diffbot/knowledge-net/master/dataset/test-no-facts.json"
}
_VERSION = datasets.Version("1.1.0")
_CLASS_LABELS = [
"NO_RELATION",
"DATE_OF_BIRTH",
"DATE_OF_DEATH",
"PLACE_OF_RESIDENCE",
"PLACE_OF_BIRTH",
"NATIONALITY",
"EMPLOYEE_OR_MEMBER_OF",
"EDUCATED_AT",
"POLITICAL_AFFILIATION",
"CHILD_OF",
"SPOUSE",
"DATE_FOUNDED",
"HEADQUARTERS",
"SUBSIDIARY_OF",
"FOUNDED_BY",
"CEO"
]
_NER_CLASS_LABELS = [
"O",
"PER",
"ORG",
"LOC",
"DATE"
]
def get_entity_types_from_relation(relation_label):
if relation_label == "DATE_OF_BIRTH":
subj_type = "PER"
obj_type = "DATE"
elif relation_label == "DATE_OF_DEATH":
subj_type = "PER"
obj_type = "DATE"
elif relation_label == "PLACE_OF_RESIDENCE":
subj_type = "PER"
obj_type = "LOC"
elif relation_label == "PLACE_OF_BIRTH":
subj_type = "PER"
obj_type = "LOC"
elif relation_label == "NATIONALITY":
subj_type = "PER"
obj_type = "LOC"
elif relation_label == "EMPLOYEE_OR_MEMBER_OF":
subj_type = "PER"
obj_type = "ORG"
elif relation_label == "EDUCATED_AT":
subj_type = "PER"
obj_type = "ORG"
elif relation_label == "POLITICAL_AFFILIATION":
subj_type = "PER"
obj_type = "ORG"
elif relation_label == "CHILD_OF":
subj_type = "PER"
obj_type = "PER"
elif relation_label == "SPOUSE":
subj_type = "PER"
obj_type = "PER"
elif relation_label == "DATE_FOUNDED":
subj_type = "ORG"
obj_type = "DATE"
elif relation_label == "HEADQUARTERS":
subj_type = "ORG"
obj_type = "LOC"
elif relation_label == "SUBSIDIARY_OF":
subj_type = "ORG"
obj_type = "ORG"
elif relation_label == "FOUNDED_BY":
subj_type = "ORG"
obj_type = "PER"
elif relation_label == "CEO":
subj_type = "ORG"
obj_type = "PER"
else:
raise ValueError(f"Unknown relation label: {relation_label}")
return subj_type, obj_type
def remove_contiguous_whitespaces(text):
# +1 to account for regular whitespace at the beginning
contiguous_whitespaces_indices = [(m.start(0) + 1, m.end(0)) for m in re.finditer(' +', text)]
cleaned_text = re.sub(" +", " ", text)
return cleaned_text, contiguous_whitespaces_indices
def fix_char_index(char_index, contiguous_whitespaces_indices):
new_char_index = char_index
offset = 0
for ws_start, ws_end in contiguous_whitespaces_indices:
if char_index >= ws_end:
offset = offset + (ws_end - ws_start)
new_char_index -= offset
return new_char_index
class KnowledgeNet(datasets.GeneratorBasedBuilder):
"""The KnowledgeNet dataset for automatically populating a knowledge base"""
BUILDER_CONFIGS = [
datasets.BuilderConfig(
name="knet", version=_VERSION, description="The original KnowledgeNet formatted for RE."
),
datasets.BuilderConfig(
name="knet_re", version=_VERSION, description="The original KnowledgeNet formatted for RE."
),
datasets.BuilderConfig(
name="knet_tokenized", version=_VERSION, description="KnowledgeNet tokenized and reformatted."
),
]
DEFAULT_CONFIG_NAME = "knet" # type: ignore
def _info(self):
if self.config.name == "knet_tokenized":
features = datasets.Features(
{
"doc_id": datasets.Value("string"),
"passage_id": datasets.Value("string"),
"fact_id": datasets.Value("string"),
"tokens": datasets.Sequence(datasets.Value("string")),
"subj_start": datasets.Value("int32"),
"subj_end": datasets.Value("int32"),
"subj_type": datasets.ClassLabel(names=_NER_CLASS_LABELS),
"subj_uri": datasets.Value("string"),
"obj_start": datasets.Value("int32"),
"obj_end": datasets.Value("int32"),
"obj_type": datasets.ClassLabel(names=_NER_CLASS_LABELS),
"obj_uri": datasets.Value("string"),
"relation": datasets.ClassLabel(names=_CLASS_LABELS),
}
)
elif self.config.name == "knet_re":
features = datasets.Features(
{
"documentId": datasets.Value("string"),
"passageId": datasets.Value("string"),
"factId": datasets.Value("string"),
"passageText": datasets.Value("string"),
"humanReadable": datasets.Value("string"),
"annotatedPassage": datasets.Value("string"),
"subjectStart": datasets.Value("int32"),
"subjectEnd": datasets.Value("int32"),
"subjectText": datasets.Value("string"),
"subjectType": datasets.ClassLabel(names=_NER_CLASS_LABELS),
"subjectUri": datasets.Value("string"),
"objectStart": datasets.Value("int32"),
"objectEnd": datasets.Value("int32"),
"objectText": datasets.Value("string"),
"objectType": datasets.ClassLabel(names=_NER_CLASS_LABELS),
"objectUri": datasets.Value("string"),
"relation": datasets.ClassLabel(names=_CLASS_LABELS),
}
)
else:
features = datasets.Features(
{
"fold": datasets.Value("int32"),
"documentId": datasets.Value("string"),
"source": datasets.Value("string"),
"documentText": datasets.Value("string"),
"passages": [{
"passageId": datasets.Value("string"),
"passageStart": datasets.Value("int32"),
"passageEnd": datasets.Value("int32"),
"passageText": datasets.Value("string"),
"exhaustivelyAnnotatedProperties": [{
"propertyId": datasets.Value("string"),
"propertyName": datasets.Value("string"),
"propertyDescription": datasets.Value("string"),
}],
"facts": [{
"factId": datasets.Value("string"),
"propertyId": datasets.Value("string"),
"humanReadable": datasets.Value("string"),
"annotatedPassage": datasets.Value("string"),
"subjectStart": datasets.Value("int32"),
"subjectEnd": datasets.Value("int32"),
"subjectText": datasets.Value("string"),
"subjectUri": datasets.Value("string"),
"objectStart": datasets.Value("int32"),
"objectEnd": datasets.Value("int32"),
"objectText": datasets.Value("string"),
"objectUri": datasets.Value("string"),
}],
}],
}
)
return datasets.DatasetInfo(
# This is the description that will appear on the datasets page.
description=_DESCRIPTION,
# This defines the different columns of the dataset and their types
features=features, # Here we define them above because they are different between the two configurations
# If there's a common (input, target) tuple from the features,
# specify them here. They'll be used if as_supervised=True in
# builder.as_dataset.
supervised_keys=None,
# Homepage of the dataset for documentation
homepage=_HOMEPAGE,
# License for the dataset if available
license=_LICENSE,
# Citation for the dataset
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
# If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
# dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLs
# It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
# By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
downloaded_files = dl_manager.download_and_extract(_URLS)
# splits = [datasets.Split.TRAIN, datasets.Split.TEST]
splits = [datasets.Split.TRAIN]
return [datasets.SplitGenerator(name=i, gen_kwargs={"filepath": downloaded_files[str(i)], "split": i})
for i in splits]
def _generate_examples(self, filepath, split):
"""Yields examples."""
# This method will receive as arguments the `gen_kwargs` defined in the previous `_split_generators` method.
# It is in charge of opening the given file and yielding (key, example) tuples from the dataset
# The key is not important, it's more here for legacy reason (legacy from tfds)
if self.config.name == "knet_tokenized":
from spacy.lang.en import English
word_splitter = English()
else:
word_splitter = None
with open(filepath, encoding="utf-8") as f:
for line in f:
doc = json.loads(line)
if self.config.name == "knet":
yield doc["documentId"], doc
else:
for passage in doc["passages"]:
# Skip passages without facts right away
if len(passage["facts"]) == 0:
continue
text = passage["passageText"]
passage_start = passage["passageStart"]
if self.config.name == "knet_tokenized":
cleaned_text, contiguous_ws_indices = remove_contiguous_whitespaces(text)
spacy_doc = word_splitter(cleaned_text)
word_tokens = [t.text for t in spacy_doc]
for fact in passage["facts"]:
subj_start = fix_char_index(fact["subjectStart"] - passage_start, contiguous_ws_indices)
subj_end = fix_char_index(fact["subjectEnd"] - passage_start, contiguous_ws_indices)
obj_start = fix_char_index(fact["objectStart"] - passage_start, contiguous_ws_indices)
obj_end = fix_char_index(fact["objectEnd"] - passage_start, contiguous_ws_indices)
# Get exclusive token spans from char spans
subj_span = spacy_doc.char_span(subj_start, subj_end, alignment_mode="expand")
obj_span = spacy_doc.char_span(obj_start, obj_end, alignment_mode="expand")
relation_label = fact["humanReadable"].split(">")[1][2:]
subj_type, obj_type = get_entity_types_from_relation(relation_label)
id_ = fact["factId"]
yield id_, {
"doc_id": doc["documentId"],
"passage_id": passage["passageId"],
"fact_id": id_,
"tokens": word_tokens,
"subj_start": subj_span.start,
"subj_end": subj_span.end,
"subj_type": subj_type,
"subj_uri": fact["subjectUri"],
"obj_start": obj_span.start,
"obj_end": obj_span.end,
"obj_type": obj_type,
"obj_uri": fact["objectUri"],
"relation": relation_label
}
else:
for fact in passage["facts"]:
relation_label = fact["humanReadable"].split(">")[1][2:]
subj_type, obj_type = get_entity_types_from_relation(relation_label)
id_ = fact["factId"]
yield id_, {
"documentId": doc["documentId"],
"passageId": passage["passageId"],
"passageText": passage["passageText"],
"factId": id_,
"humanReadable": fact["humanReadable"],
"annotatedPassage": fact["annotatedPassage"],
"subjectStart": fact["subjectStart"] - passage_start,
"subjectEnd": fact["subjectEnd"] - passage_start,
"subjectText": fact["subjectText"],
"subjectType": subj_type,
"subjectUri": fact["subjectUri"],
"objectStart": fact["objectStart"] - passage_start,
"objectEnd": fact["objectEnd"] - passage_start,
"objectText": fact["objectText"],
"objectType": obj_type,
"objectUri": fact["objectUri"],
"relation": relation_label
}
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