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README.md

@@ -1,3 +1,116 @@
 ---
-license: cc-by-4.0
+annotations_creators:
+- expert-generated
+- auto-generated
+language:
+- en
+language_creators:
+- machine-generated
+license:
+- cc-by-4.0
+multilinguality:
+- monolingual
+pretty_name: 'The SciQA Scientific Question Answering Benchmark for Scholarly Knowledge'
+size_categories:
+- 1K<n<10K
+source_datasets:
+- original
+tags:
+- knowledge-base-qa
+task_categories:
+- question-answering
+task_ids: []
 ---
+
+# Dataset Card for SciQA
+
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-fields)
+  - [Data Splits](#data-splits)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+  - [Contributions](#contributions)
+
+## Dataset Description
+
+- **Homepage:** [SciQA Homepage]()
+- **Repository:** [SciQA Repository](https://zenodo.org/record/7744048)
+- **Paper:** The SciQA Scientific Question Answering Benchmark for Scholarly Knowledge
+- **Point of Contact:** [Yaser Jaradeh](mailto:[email protected])
+
+### Dataset Summary
+
+SciQA contains 2,565 SPARQL query - question pairs along with answers fetched from the open research knowledge graph (ORKG) via a Virtuoso SPARQL endpoint, it is a collection of both handcrafted and autogenerated questions and queries. The dataset is split into 70% training, 10% validation and 20% test examples. 
+
+## Dataset Structure
+
+### Data Instances
+
+An example of a question is given below:
+
+```json
+{
+    "id": "AQ2251",
+    "query_type": "Factoid",
+    "question": {
+        "string": "Provide a list of papers that have utilized the Depth DDPPO model and include the links to their code?"
+    },
+    "paraphrased_question": [],
+    "query": {
+        "sparql": "SELECT DISTINCT ?code\nWHERE {\n  ?model    a  orkgc:Model;\n            rdfs:label    ?model_lbl.\n  FILTER (str(?model_lbl) = \"Depth DDPPO\")\n  ?benchmark      orkgp:HAS_DATASET        ?dataset.\n  ?cont           orkgp:HAS_BENCHMARK      ?benchmark.\n  ?cont           orkgp:HAS_MODEL          ?model;\n                  orkgp:HAS_SOURCE_CODE    ?code.\n}"
+    },
+    "template_id": "T07",
+    "auto_generated": true,
+    "query_shape": "Tree",
+    "query_class": "WHICH-WHAT",
+    "number_of_patterns": 4,
+}
+
+```
+### Data Fields
+
+- `id`: the id of the question
+- `question`: a string containing the question
+- `paraphrased_question`: a set of paraphrased versions of the question
+- `query`: a SPARQL query that answers the question
+- `query_type`: the type of the query
+- `query_template`: an optional template of the query
+- `query_shape`: a string indicating the shape of the query
+- `query_class`: a string indicating the class of the query
+- `auto_generated`: a boolean indicating whether the question is auto-generated or not
+- `number_of_patterns`: an integer number indicating the number of gtaph patterns in the query
+
+### Data Splits
+
+The dataset is split into 70% training, 10% validation and 20% test questions.
+
+## Additional Information
+
+### Licensing Information
+
+SciQA is licensed under the [Creative Commons Attribution 4.0 International License (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/).
+
+### Citation Information
+
+In review.
+
+### Contributions
+
+Thanks to [@YaserJaradeh](https://github.com/YaserJaradeh) for adding this dataset.

SciQA.py

@@ -0,0 +1,117 @@
+# 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
+"""The SciQA Scientific Question Answering Benchmark for Scholarly Knowledge"""
+
+
+import json
+import os
+
+import datasets
+
+
+logger = datasets.logging.get_logger(__name__)
+
+
+_CITATION = """
+    @article{SciQA,
+        title={The SciQA Scientific Question Answering Benchmark for Scholarly Knowledge},
+        author={Auer, Sören and Barone, Dante A. C. and Bartz, Cassiano and Cortes, Eduardo G. and Jaradeh, Mohamad Yaser and Karras, Oliver and Koubarakis, Manolis and Mouromtsev, Dmitry and Pliukhin, Dmitrii and Radyush, Daniil and et al.},
+        year={2023}
+"""
+
+_DESCRIPTION = """\
+    SciQA contains 2,565 SPARQL query - question pairs along with answers fetched from the open research knowledge graph (ORKG) \
+    via a Virtuoso SPARQL endpoint, it is a collection of both handcrafted and autogenerated questions and queries. \
+    The dataset is split into 70% training, 10% validation and 20% test examples. The dataset is available as JSON files.
+"""
+
+_URL = "https://zenodo.org/record/7744048/files/SciQA-dataset.zip"
+
+
+class SciQA(datasets.GeneratorBasedBuilder):
+    """
+        The SciQA Scientific Question Answering Benchmark for Scholarly Knowledge.
+    """
+
+    VERSION = datasets.Version("1.0.0")
+
+    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.features.Sequence(datasets.Value("string")),
+                    "query": datasets.dataset_dict.DatasetDict({
+                        "sparql": datasets.Value("string")
+                    }),
+                    "template_id": datasets.Value("string"),
+                    "query_shape": datasets.Value("string"),
+                    "query_class": datasets.Value("string"),
+                    "auto_generated": datasets.Value("bool"),
+                    "number_of_patterns": datasets.Value("int32")
+                }
+            ),
+            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, "SciQA-dataset")
+
+        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"],
+                    "query_shape": row["query_shape"],
+                    "query_class": row["query_class"],
+                    "auto_generated": row["auto_generated"],
+                    "number_of_patterns": row["number_of_patterns"]
+                }