Update parquet files

README.md

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----
-annotations_creators:
-- found
-language_creators:
-- found
-language:
-- en
-license:
-- cc-by-4.0
-multilinguality:
-- monolingual
-size_categories:
-- 1M<n<10M
-source_datasets:
-- original
-task_categories:
-- other
-task_ids: []
-paperswithcode_id: ascentkb
-pretty_name: Ascent KB
-tags:
-- knowledge-base
-dataset_info:
-- config_name: canonical
-  features:
-  - name: arg1
-    dtype: string
-  - name: rel
-    dtype: string
-  - name: arg2
-    dtype: string
-  - name: support
-    dtype: int64
-  - name: facets
-    list:
-    - name: value
-      dtype: string
-    - name: type
-      dtype: string
-    - name: support
-      dtype: int64
-  - name: source_sentences
-    list:
-    - name: text
-      dtype: string
-    - name: source
-      dtype: string
-  splits:
-  - name: train
-    num_bytes: 2976697816
-    num_examples: 8904060
-  download_size: 710727536
-  dataset_size: 2976697816
-- config_name: open
-  features:
-  - name: subject
-    dtype: string
-  - name: predicate
-    dtype: string
-  - name: object
-    dtype: string
-  - name: support
-    dtype: int64
-  - name: facets
-    list:
-    - name: value
-      dtype: string
-    - name: type
-      dtype: string
-    - name: support
-      dtype: int64
-  - name: source_sentences
-    list:
-    - name: text
-      dtype: string
-    - name: source
-      dtype: string
-  splits:
-  - name: train
-    num_bytes: 2882678298
-    num_examples: 8904060
-  download_size: 710727536
-  dataset_size: 2882678298
----
-
-# Dataset Card for Ascent KB
-
-## 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:** https://ascent.mpi-inf.mpg.de/
-- **Repository:** https://github.com/phongnt570/ascent
-- **Paper:** https://arxiv.org/abs/2011.00905
-- **Point of Contact:** http://tuan-phong.com
-
-### Dataset Summary
-
-This dataset contains 8.9M commonsense assertions extracted  by the Ascent pipeline developed at the [Max Planck Institute for Informatics](https://www.mpi-inf.mpg.de/departments/databases-and-information-systems/).
-The focus of this dataset is on everyday concepts such as *elephant*, *car*, *laptop*, etc.
-The current version of Ascent KB (v1.0.0) is approximately **19 times larger  than ConceptNet** (note that, in this comparison, non-commonsense knowledge in ConceptNet such as lexical relations is excluded).
-
-For more details, take a look at
-[the research paper](https://arxiv.org/abs/2011.00905) and
-[the website](https://ascent.mpi-inf.mpg.de).
-
-### Supported Tasks and Leaderboards
-
-The dataset can be used in a wide range of downstream tasks such as commonsense question answering or dialogue systems.
-
-### Languages
-
-The dataset is in English.
-
-## Dataset Structure
-
-### Data Instances
-There are two configurations available for this dataset:
-1. `canonical` (default): This part contains `<arg1 ; rel ; arg2>`
-  assertions where the relations (`rel`) were mapped to 
-  [ConceptNet relations](https://github.com/commonsense/conceptnet5/wiki/Relations)
-  with slight modifications:
-    - Introducing 2 new relations: `/r/HasSubgroup`, `/r/HasAspect`.
-    - All `/r/HasA` relations were replaced with `/r/HasAspect`. 
-      This is motivated by the [ATOMIC-2020](https://allenai.org/data/atomic-2020)
-      schema, although they grouped all `/r/HasA` and
-      `/r/HasProperty` into `/r/HasProperty`.
-    - The `/r/UsedFor` relation was replaced with `/r/ObjectUse`
-      which is broader (could be either _"used for"_, _"used in"_, or _"used as"_, ect.).
-      This is also taken from ATOMIC-2020.
-2. `open`: This part contains open assertions of the form
-  `<subject ; predicate ; object>` extracted directly from web
-  contents. This is the original form of the `canonical` triples. 
-
-In both configurations, each assertion is equipped with 
-extra information including: a set of semantic `facets`
-(e.g., *LOCATION*, *TEMPORAL*, etc.), its `support` (i.e., number of occurrences),
-and a list of `source_sentences`.
-
-An example row in the `canonical` configuration:
-
-```JSON
-{
-  "arg1": "elephant",
-  "rel": "/r/HasProperty",
-  "arg2": "intelligent",
-  "support": 15,
-  "facets": [
-    {
-      "value": "extremely",
-      "type": "DEGREE",
-      "support": 11
-    }
-  ],
-  "source_sentences": [
-    {
-      "text": "Elephants are extremely intelligent animals.",
-      "source": "https://www.softschools.com/facts/animals/asian_elephant_facts/2310/"
-    },
-    {
-      "text": "Elephants are extremely intelligent creatures and an elephant's brain can weigh as much as 4-6 kg.",
-      "source": "https://www.elephantsforafrica.org/elephant-facts/"
-    }
-  ]
-}
-```
-
-### Data Fields
-
-- **For `canonical` configuration**
-    - `arg1`: the first argument to the relationship, e.g., *elephant*
-    - `rel`: the canonical relation, e.g., */r/HasProperty*
-    - `arg2`: the second argument to the relationship, e.g., *intelligence*
-    - `support`: the number of occurrences of the assertion, e.g., *15*
-    - `facets`: an array of semantic facets, each contains
-      - `value`: facet value, e.g., *extremely*
-      - `type`: facet type, e.g., *DEGREE*
-      - `support`: the number of occurrences of the facet, e.g., *11*
-    - `source_sentences`: an array of source sentences from which the assertion was
-      extracted, each contains
-      - `text`: the raw text of the sentence
-      - `source`: the URL to its parent document
-
-- **For `open` configuration**
-    - The fields of this configuration are the same as the `canonical`
-      configuration's, except that
-      the (`arg1`, `rel`, `arg2`) fields are replaced with the
-      (`subject`, `predicate`, `object`) fields
-      which are free
-      text phrases extracted directly from the source sentences
-      using an Open Information Extraction (OpenIE) tool.
-
-### Data Splits
-
-There are no splits. All data points come to a default split called `train`.
-
-## Dataset Creation
-
-### Curation Rationale
-
-The commonsense knowledge base was created to assist in development of robust and reliable AI.
-
-### Source Data
-
-#### Initial Data Collection and Normalization
-
-Texts were collected from the web using the Bing Search API, and went through various cleaning steps before being processed by an OpenIE tool to get open assertions.
-The assertions were then grouped into semantically equivalent clusters.
-Take a look at the research paper for more details.
-
-#### Who are the source language producers?
-
-Web users.
-
-### Annotations
-
-#### Annotation process
-
-None.
-
-#### Who are the annotators?
-
-None.
-
-### Personal and Sensitive Information
-
-Unknown.
-
-## Considerations for Using the Data
-
-### Social Impact of Dataset
-
-[Needs More Information]
-
-### Discussion of Biases
-
-[Needs More Information]
-
-### Other Known Limitations
-
-[Needs More Information]
-
-## Additional Information
-
-### Dataset Curators
-
-The knowledge base has been developed by researchers at the
-[Max Planck Institute for Informatics](https://www.mpi-inf.mpg.de/departments/databases-and-information-systems/).
-
-Contact [Tuan-Phong Nguyen](http://tuan-phong.com) in case of questions and comments.
-
-### Licensing Information
-
-[The Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/)
-
-### Citation Information
-
-```
-@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},
-}
-```
-
-### Contributions
-
-Thanks to [@phongnt570](https://github.com/phongnt570) for adding this dataset.

ascent_kb.py

@@ -1,147 +0,0 @@
-# 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

dataset_infos.json

@@ -1 +0,0 @@
-{"canonical": {"description": "This dataset contains 8.9M commonsense assertions extracted by the Ascent pipeline (https://ascent.mpi-inf.mpg.de/).\n", "citation": "@InProceedings{nguyen2021www,\n  title={Advanced Semantics for Commonsense Knowledge Extraction},\n  author={Nguyen, Tuan-Phong and Razniewski, Simon and Weikum, Gerhard},\n  year={2021},\n  booktitle={The Web Conference 2021},\n}\n", "homepage": "https://ascent.mpi-inf.mpg.de/", "license": "The Creative Commons Attribution 4.0 International License. https://creativecommons.org/licenses/by/4.0/", "features": {"arg1": {"dtype": "string", "id": null, "_type": "Value"}, "rel": {"dtype": "string", "id": null, "_type": "Value"}, "arg2": {"dtype": "string", "id": null, "_type": "Value"}, "support": {"dtype": "int64", "id": null, "_type": "Value"}, "facets": [{"value": {"dtype": "string", "id": null, "_type": "Value"}, "type": {"dtype": "string", "id": null, "_type": "Value"}, "support": {"dtype": "int64", "id": null, "_type": "Value"}}], "source_sentences": [{"text": {"dtype": "string", "id": null, "_type": "Value"}, "source": {"dtype": "string", "id": null, "_type": "Value"}}]}, "post_processed": null, "supervised_keys": null, "builder_name": "ascent_kb", "config_name": "canonical", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 2976697816, "num_examples": 8904060, "dataset_name": "ascent_kb"}}, "download_checksums": {"https://nextcloud.mpi-klsb.mpg.de/index.php/s/dFLdTQHqiFrt3Q3/download": {"num_bytes": 710727536, "checksum": "51fd88a07bca4fa48a9157dd1d93d9bac88ad2b38b5eae662d2cbfad47895016"}}, "download_size": 710727536, "post_processing_size": null, "dataset_size": 2976697816, "size_in_bytes": 3687425352}, "open": {"description": "This dataset contains 8.9M commonsense assertions extracted by the Ascent pipeline (https://ascent.mpi-inf.mpg.de/).\n", "citation": "@InProceedings{nguyen2021www,\n  title={Advanced Semantics for Commonsense Knowledge Extraction},\n  author={Nguyen, Tuan-Phong and Razniewski, Simon and Weikum, Gerhard},\n  year={2021},\n  booktitle={The Web Conference 2021},\n}\n", "homepage": "https://ascent.mpi-inf.mpg.de/", "license": "The Creative Commons Attribution 4.0 International License. https://creativecommons.org/licenses/by/4.0/", "features": {"subject": {"dtype": "string", "id": null, "_type": "Value"}, "predicate": {"dtype": "string", "id": null, "_type": "Value"}, "object": {"dtype": "string", "id": null, "_type": "Value"}, "support": {"dtype": "int64", "id": null, "_type": "Value"}, "facets": [{"value": {"dtype": "string", "id": null, "_type": "Value"}, "type": {"dtype": "string", "id": null, "_type": "Value"}, "support": {"dtype": "int64", "id": null, "_type": "Value"}}], "source_sentences": [{"text": {"dtype": "string", "id": null, "_type": "Value"}, "source": {"dtype": "string", "id": null, "_type": "Value"}}]}, "post_processed": null, "supervised_keys": null, "builder_name": "ascent_kb", "config_name": "open", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 2882678298, "num_examples": 8904060, "dataset_name": "ascent_kb"}}, "download_checksums": {"https://nextcloud.mpi-klsb.mpg.de/index.php/s/dFLdTQHqiFrt3Q3/download": {"num_bytes": 710727536, "checksum": "51fd88a07bca4fa48a9157dd1d93d9bac88ad2b38b5eae662d2cbfad47895016"}}, "download_size": 710727536, "post_processing_size": null, "dataset_size": 2882678298, "size_in_bytes": 3593405834}}

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