Datasets:
Tasks:
Question Answering
Modalities:
Text
Languages:
English
Size:
10K - 100K
Tags:
knowledge-base-qa
License:
File size: 4,503 Bytes
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# coding=utf-8
# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
#
# 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.
# Lint as: python3
"""DBLP-QuAD: A Question Answering Dataset over the DBLP Scholarly Knowledge Graph."""
import json
import os
import datasets
logger = datasets.logging.get_logger(__name__)
_CITATION = """
@article{DBLP-QuAD,
title={DBLP-QuAD: A Question Answering Dataset over the DBLP Scholarly Knowledge Graph},
author={Banerjee, Debayan and Awale, Sushil and Usbeck, Ricardo and Biemann, Chris},
year={2023}
"""
_DESCRIPTION = """\
DBLP-QuAD is a scholarly knowledge graph question answering dataset with \
10,000 question - SPARQL query pairs targeting the DBLP knowledge graph. \
The dataset is split into 7,000 training, 1,000 validation and 2,000 test \
questions.
"""
_URL = "https://zenodo.org/record/7643971/files/DBLP-QuAD.zip"
class DBLPQuAD(datasets.GeneratorBasedBuilder):
"""
DBLP-QuAD: A Question Answering Dataset over the DBLP Scholarly Knowledge Graph.
"""
VERSION = datasets.Version("1.1.0")
def _info(self):
return datasets.DatasetInfo(
# This is the description that will appear on the datasets page.
description=_DESCRIPTION,
# datasets.features.FeatureConnectors
features=datasets.Features(
{
"id": datasets.Value("string"),
"query_type": datasets.Value("string"),
"question": datasets.dataset_dict.DatasetDict({
"string": datasets.Value("string")
}),
"paraphrased_question": datasets.dataset_dict.DatasetDict({
"string": datasets.Value("string")
}),
"query": datasets.dataset_dict.DatasetDict({
"sparql": datasets.Value("string")
}),
"template_id": datasets.Value("string"),
"entities": datasets.features.Sequence(datasets.Value("string")),
"relations": datasets.features.Sequence(datasets.Value("string")),
"temporal": datasets.Value("bool"),
"held_out": datasets.Value("bool")
}
),
supervised_keys=None,
citation=_CITATION,
)
def _split_generators(self, dl_manager):
"""Returns SplitGenerators."""
dl_dir = dl_manager.download_and_extract(_URL)
dl_dir = os.path.join(dl_dir, "DBLP-QuAD")
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
gen_kwargs={"filepath": os.path.join(dl_dir, "train", "questions.json")},
),
datasets.SplitGenerator(
name=datasets.Split.VALIDATION,
gen_kwargs={"filepath": os.path.join(dl_dir, "valid", "questions.json")},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
gen_kwargs={"filepath": os.path.join(dl_dir, "test", "questions.json")},
),
]
def _generate_examples(self, filepath):
"""Yields examples."""
with open(filepath, encoding="utf-8") as f:
data = json.load(f)["questions"]
for id_, row in enumerate(data):
yield id_, {
"id": row["id"],
"query_type": row["query_type"],
"question": row["question"],
"paraphrased_question": row["paraphrased_question"],
"query": row["query"],
"template_id": row["template_id"],
"entities": row["entities"],
"relations": row["relations"],
"temporal": row["temporal"],
"held_out": row["held_out"]
}
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