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# coding=utf-8
# Copyright 2020 The HuggingFace Datasets Authors and 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.
"""Ascent KB: A Deep Commonsense Knowledge Base"""
import json
import datasets
_CITATION = """\
@InProceedings{nguyen2021www,
title={Advanced Semantics for Commonsense Knowledge Extraction},
author={Nguyen, Tuan-Phong and Razniewski, Simon and Weikum, Gerhard},
year={2021},
booktitle={The Web Conference 2021},
}
"""
_DESCRIPTION = """\
This dataset contains 8.9M commonsense assertions extracted by the Ascent pipeline (https://ascent.mpi-inf.mpg.de/).
"""
_HOMEPAGE = "https://ascent.mpi-inf.mpg.de/"
_LICENSE = "The Creative Commons Attribution 4.0 International License. https://creativecommons.org/licenses/by/4.0/"
# 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)
_URL = "https://nextcloud.mpi-klsb.mpg.de/index.php/s/dFLdTQHqiFrt3Q3/download"
# DONE: Name of the dataset usually match the script name with CamelCase instead of snake_case
class AscentKB(datasets.GeneratorBasedBuilder):
"""Ascent KB: A Deep Commonsense Knowledge Base. Version 1.0.0."""
VERSION = datasets.Version("1.0.0")
BUILDER_CONFIGS = [
datasets.BuilderConfig(
name="canonical",
version=VERSION,
description="This KB contains <arg1 ; rel ; arg2> \
assertions where relations are canonicalized based on ConceptNet relations.",
),
datasets.BuilderConfig(
name="open",
version=VERSION,
description="This KB contains open assertions of the form \
<subject ; predicate ; object> extracted directly from web contents.",
),
]
DEFAULT_CONFIG_NAME = "canonical"
def _info(self):
if self.config.name == "canonical":
features = datasets.Features(
{
"arg1": datasets.Value("string"),
"rel": datasets.Value("string"),
"arg2": datasets.Value("string"),
"support": datasets.Value("int64"),
"facets": [
{
"value": datasets.Value("string"),
"type": datasets.Value("string"),
"support": datasets.Value("int64"),
}
],
"source_sentences": [{"text": datasets.Value("string"), "source": datasets.Value("string")}],
}
)
else: # features for the "open" part
features = datasets.Features(
{
"subject": datasets.Value("string"),
"predicate": datasets.Value("string"),
"object": datasets.Value("string"),
"support": datasets.Value("int64"),
"facets": [
{
"value": datasets.Value("string"),
"type": datasets.Value("string"),
"support": datasets.Value("int64"),
}
],
"source_sentences": [{"text": datasets.Value("string"), "source": datasets.Value("string")}],
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
supervised_keys=None,
homepage=_HOMEPAGE,
license=_LICENSE,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
# my_urls = _URLs[self.config.name]
# data_file = dl_manager.download_and_extract(my_urls)
data_file = dl_manager.download_and_extract(_URL)
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={
"filepath": data_file,
"split": "train",
},
),
]
# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
def _generate_examples(self, filepath, split):
"""Yields examples as (key, example) tuples."""
# This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
# The `key` is here for legacy reason (tfds) and is not important in itself.
with open(filepath, encoding="utf-8") as f:
for id_, row in enumerate(f):
data = json.loads(row)
if self.config.name == "canonical":
data.pop("subject")
data.pop("predicate")
data.pop("object")
yield id_, data
else: # "open"
data.pop("arg1")
data.pop("rel")
data.pop("arg2")
yield id_, data
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