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# 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.
# TODO: Address all TODOs and remove all explanatory comments
"""TODO: Add a description here."""
import csv
import json
import os
import datasets
# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
@article{ilievski2021cskg,
title={CSKG: The CommonSense Knowledge Graph},
author={Ilievski, Filip and Szekely, Pedro and Zhang, Bin},
journal={Extended Semantic Web Conference (ESWC)},
year={2021}
}
"""
# TODO: Add description of the dataset here
# You can copy an official description
_DESCRIPTION = """\
CSKG is a commonsense knowledge graph that combines seven popular sources into a consolidated representation: ATOMIC, ConceptNet, FrameNet, Roget, Visual Genome, Wikidata (We use the Wikidata-CS subset), and WordNet. CSKG is represented as a hyper-relational graph, by using the KGTK data model and file specification. Its creation is entirely supported by KGTK operations.
"""
# TODO: Add a link to an official homepage for the dataset here
_HOMEPAGE = "https://cskg.readthedocs.io/en/latest/"
# TODO: Add the licence for the dataset here if you can find it
_LICENSE = "Creative Commons Attribution-ShareAlike 4.0 International License"
# TODO: Add link to the official dataset URLs here
# The HuggingFace Datasets library doesn't host the datasets but only points to the original files.
# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
# _URLS = {
# "first_domain": "https://huggingface.co/great-new-dataset-first_domain.zip",
# "second_domain": "https://huggingface.co/great-new-dataset-second_domain.zip",
# }
# _URLS = {
# # "cskg": "https://zenodo.org/record/4331372/files/cskg.tsv.gz",
# }
# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
class cskg_2(datasets.GeneratorBasedBuilder):
"""a commonsense knowledge graph"""
VERSION = datasets.Version("1.1.0")
# This is an example of a dataset with multiple configurations.
# If you don't want/need to define several sub-sets in your dataset,
# just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes.
# If you need to make complex sub-parts in the datasets with configurable options
# You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig
# BUILDER_CONFIG_CLASS = MyBuilderConfig
# You will be able to load one or the other configurations in the following list with
# data = datasets.load_dataset('my_dataset', 'first_domain')
# data = datasets.load_dataset('my_dataset', 'second_domain')
BUILDER_CONFIGS = [
datasets.BuilderConfig(name="cskg_2", version=VERSION, description="The relationships defined by cskg"),
# datasets.BuilderConfig(name="second_domain", version=VERSION, description="This part of my dataset covers a second domain"),
]
DEFAULT_CONFIG_NAME = "cskg_2" # It's not mandatory to have a default configuration. Just use one if it make sense.
def _info(self):
# TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
if self.config.name == "cskg_2": # This is the name of the configuration selected in BUILDER_CONFIGS above
features = datasets.Features(
{
"id": datasets.Value("string"),
"node1": datasets.Value("string"),
"relation": datasets.Value("string"),
"node2": datasets.Value("string"),
"node1;label": datasets.Value("string"),
"node2;label": datasets.Value("string"),
"relation;label": datasets.Value("string"),
"relation;dimension": datasets.Value("string"),
"source": datasets.Value("string"),
"sentence": datasets.Value("string"),
# These are the features of your dataset like images, labels ...
}
)
# else: # This is an example to show how to have different features for "first_domain" and "second_domain"
# features = datasets.Features(
# {
# "sentence": datasets.Value("string"),
# "option2": datasets.Value("string"),
# "second_domain_answer": datasets.Value("string")
# # These are the features of your dataset like images, labels ...
# }
# )
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, uncomment supervised_keys line below and
# specify them. They'll be used if as_supervised=True in builder.as_dataset.
# supervised_keys=("sentence", "label"),
# 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):
# TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
# 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
# urls = _URLS[self.config.name]
data_dir = 'cskg_connected.kgtk'
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"filepath": data_dir,
"split": "train",
},
),
# datasets.SplitGenerator(
# name=datasets.Split.VALIDATION,
# # These kwargs will be passed to _generate_examples
# gen_kwargs={
# "filepath": os.path.join(data_dir, "dev.jsonl"),
# "split": "dev",
# },
# ),
# datasets.SplitGenerator(
# name=datasets.Split.TEST,
# # These kwargs will be passed to _generate_examples
# gen_kwargs={
# "filepath": os.path.join(data_dir, "test.jsonl"),
# "split": "test"
# },
# ),
]
# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
def _generate_examples(self, filepath, split):
# TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
# The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
# jump the first row
with open(filepath, 'rb') as f:
for id_, row in enumerate(f):
if id_ == 0:
continue
if self.config.name == "cskg_2":
row = row.split(b"\t")
# Yields examples as (key, example) tuples
yield id_, {
# "sentence": data["sentence"],
# "option1": data["option1"],
# "answer": "" if split == "test" else data["answer"],
"id": row[0].decode("utf-8"),
"node1": row[1].decode("utf-8"),
"relation": row[2].decode("utf-8"),
"node2": row[3].decode("utf-8"),
"node1;label": row[4].decode("utf-8"),
"node2;label": row[5].decode("utf-8"),
"relation;label": row[6].decode("utf-8"),
"relation;dimension": row[7].decode("utf-8"),
"source": row[8].decode("utf-8"),
"sentence": row[9].decode("utf-8"),
}
# else:
# yield key, {
# "sentence": data["sentence"],
# "option2": data["option2"],
# "second_domain_answer": "" if split == "test" else data["second_domain_answer"],
# }
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