Datasets:

DFKI-SLT
/

knowledge_net

	# 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
	}