DBLP-QuAD.py

# coding=utf-8
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#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#
#     http://www.apache.org/licenses/LICENSE-2.0
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# 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"]
                }