Datasets:
Tasks:
Other
Modalities:
Text
Formats:
parquet
Languages:
English
Size:
10M - 100M
ArXiv:
Tags:
knowledge-base
License:
Commit
•
64a5bc2
0
Parent(s):
Update files from the datasets library (from 1.7.0)
Browse filesRelease notes: https://github.com/huggingface/datasets/releases/tag/1.7.0
- .gitattributes +27 -0
- README.md +228 -0
- ascent_kb.py +147 -0
- dataset_infos.json +1 -0
- dummy/canonical/1.0.0/dummy_data.zip +3 -0
- dummy/open/1.0.0/dummy_data.zip +3 -0
.gitattributes
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README.md
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---
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annotations_creators:
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- found
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language_creators:
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- found
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languages:
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- en
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licenses:
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- cc-by-4-0
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multilinguality:
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- monolingual
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size_categories:
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- 1M<n<10M
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source_datasets:
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- original
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task_categories:
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- other
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task_ids:
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- other-other-knowledge-base
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paperswithcode_id: ascentkb
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---
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# Dataset Card for Ascent KB
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## Table of Contents
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- [Dataset Description](#dataset-description)
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- [Dataset Summary](#dataset-summary)
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- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
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- [Languages](#languages)
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- [Dataset Structure](#dataset-structure)
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- [Data Instances](#data-instances)
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- [Data Fields](#data-fields)
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- [Data Splits](#data-splits)
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- [Dataset Creation](#dataset-creation)
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- [Curation Rationale](#curation-rationale)
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- [Source Data](#source-data)
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- [Annotations](#annotations)
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- [Personal and Sensitive Information](#personal-and-sensitive-information)
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- [Considerations for Using the Data](#considerations-for-using-the-data)
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- [Social Impact of Dataset](#social-impact-of-dataset)
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- [Discussion of Biases](#discussion-of-biases)
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- [Other Known Limitations](#other-known-limitations)
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- [Additional Information](#additional-information)
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- [Dataset Curators](#dataset-curators)
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- [Licensing Information](#licensing-information)
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- [Citation Information](#citation-information)
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- [Contributions](#contributions)
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## Dataset Description
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- **Homepage:** https://ascent.mpi-inf.mpg.de/
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- **Repository:** https://github.com/phongnt570/ascent
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- **Paper:** https://arxiv.org/abs/2011.00905
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- **Point of Contact:** http://tuan-phong.com
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### Dataset Summary
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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/).
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The focus of this dataset is on everyday concepts such as *elephant*, *car*, *laptop*, etc.
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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).
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For more details, take a look at
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[the research paper](https://arxiv.org/abs/2011.00905) and
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[the website](https://ascent.mpi-inf.mpg.de).
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### Supported Tasks and Leaderboards
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The dataset can be used in a wide range of downstream tasks such as commonsense question answering or dialogue systems.
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### Languages
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The dataset is in English.
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## Dataset Structure
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### Data Instances
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There are two configurations available for this dataset:
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1. `canonical` (default): This part contains `<arg1 ; rel ; arg2>`
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assertions where the relations (`rel`) were mapped to
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[ConceptNet relations](https://github.com/commonsense/conceptnet5/wiki/Relations)
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with slight modifications:
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- Introducing 2 new relations: `/r/HasSubgroup`, `/r/HasAspect`.
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- All `/r/HasA` relations were replaced with `/r/HasAspect`.
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This is motivated by the [ATOMIC-2020](https://allenai.org/data/atomic-2020)
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schema, although they grouped all `/r/HasA` and
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`/r/HasProperty` into `/r/HasProperty`.
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- The `/r/UsedFor` relation was replaced with `/r/ObjectUse`
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which is broader (could be either _"used for"_, _"used in"_, or _"used as"_, ect.).
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This is also taken from ATOMIC-2020.
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2. `open`: This part contains open assertions of the form
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`<subject ; predicate ; object>` extracted directly from web
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contents. This is the original form of the `canonical` triples.
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In both configurations, each assertion is equipped with
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extra information including: a set of semantic `facets`
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(e.g., *LOCATION*, *TEMPORAL*, etc.), its `support` (i.e., number of occurrences),
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and a list of `source_sentences`.
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An example row in the `canonical` configuration:
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```JSON
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{
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"arg1": "elephant",
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"rel": "/r/HasProperty",
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"arg2": "intelligent",
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"support": 15,
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"facets": [
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{
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"value": "extremely",
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"type": "DEGREE",
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"support": 11
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}
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],
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"source_sentences": [
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{
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"text": "Elephants are extremely intelligent animals.",
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"source": "https://www.softschools.com/facts/animals/asian_elephant_facts/2310/"
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},
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{
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"text": "Elephants are extremely intelligent creatures and an elephant's brain can weigh as much as 4-6 kg.",
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"source": "https://www.elephantsforafrica.org/elephant-facts/"
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}
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]
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}
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```
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### Data Fields
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- **For `canonical` configuration**
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- `arg1`: the first argument to the relationship, e.g., *elephant*
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- `rel`: the canonical relation, e.g., */r/HasProperty*
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- `arg2`: the second argument to the relationship, e.g., *intelligence*
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- `support`: the number of occurrences of the assertion, e.g., *15*
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- `facets`: an array of semantic facets, each contains
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- `value`: facet value, e.g., *extremely*
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- `type`: facet type, e.g., *DEGREE*
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- `support`: the number of occurrences of the facet, e.g., *11*
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- `source_sentences`: an array of source sentences from which the assertion was
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extracted, each contains
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- `text`: the raw text of the sentence
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- `source`: the URL to its parent document
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- **For `open` configuration**
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- The fields of this configuration are the same as the `canonical`
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configuration's, except that
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the (`arg1`, `rel`, `arg2`) fields are replaced with the
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(`subject`, `predicate`, `object`) fields
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which are free
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text phrases extracted directly from the source sentences
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using an Open Information Extraction (OpenIE) tool.
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### Data Splits
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There are no splits. All data points come to a default split called `train`.
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## Dataset Creation
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### Curation Rationale
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The commonsense knowledge base was created to assist in development of robust and reliable AI.
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### Source Data
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#### Initial Data Collection and Normalization
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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.
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The assertions were then grouped into semantically equivalent clusters.
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Take a look at the research paper for more details.
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#### Who are the source language producers?
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Web users.
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### Annotations
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#### Annotation process
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None.
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#### Who are the annotators?
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None.
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### Personal and Sensitive Information
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Unknown.
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## Considerations for Using the Data
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### Social Impact of Dataset
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[Needs More Information]
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### Discussion of Biases
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[Needs More Information]
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### Other Known Limitations
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[Needs More Information]
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## Additional Information
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### Dataset Curators
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The knowledge base has been developed by researchers at the
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[Max Planck Institute for Informatics](https://www.mpi-inf.mpg.de/departments/databases-and-information-systems/).
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Contact [Tuan-Phong Nguyen](http://tuan-phong.com) in case of questions and comments.
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### Licensing Information
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[The Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/)
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### Citation Information
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```
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@InProceedings{nguyen2021www,
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title={Advanced Semantics for Commonsense Knowledge Extraction},
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author={Nguyen, Tuan-Phong and Razniewski, Simon and Weikum, Gerhard},
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year={2021},
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booktitle={The Web Conference 2021},
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}
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```
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### Contributions
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Thanks to [@phongnt570](https://github.com/phongnt570) for adding this dataset.
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ascent_kb.py
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# coding=utf-8
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# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Ascent KB: A Deep Commonsense Knowledge Base"""
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import json
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import datasets
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_CITATION = """\
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@InProceedings{nguyen2021www,
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title={Advanced Semantics for Commonsense Knowledge Extraction},
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author={Nguyen, Tuan-Phong and Razniewski, Simon and Weikum, Gerhard},
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year={2021},
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booktitle={The Web Conference 2021},
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}
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"""
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_DESCRIPTION = """\
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This dataset contains 8.9M commonsense assertions extracted by the Ascent pipeline (https://ascent.mpi-inf.mpg.de/).
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"""
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_HOMEPAGE = "https://ascent.mpi-inf.mpg.de/"
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+
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_LICENSE = "The Creative Commons Attribution 4.0 International License. https://creativecommons.org/licenses/by/4.0/"
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# The HuggingFace dataset library don't host the datasets but only point to the original files
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# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
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_URL = "https://nextcloud.mpi-klsb.mpg.de/index.php/s/dFLdTQHqiFrt3Q3/download"
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# DONE: Name of the dataset usually match the script name with CamelCase instead of snake_case
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class AscentKB(datasets.GeneratorBasedBuilder):
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"""Ascent KB: A Deep Commonsense Knowledge Base. Version 1.0.0."""
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VERSION = datasets.Version("1.0.0")
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BUILDER_CONFIGS = [
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datasets.BuilderConfig(
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name="canonical",
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version=VERSION,
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description="This KB contains <arg1 ; rel ; arg2> \
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assertions where relations are canonicalized based on ConceptNet relations.",
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),
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datasets.BuilderConfig(
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name="open",
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version=VERSION,
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description="This KB contains open assertions of the form \
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<subject ; predicate ; object> extracted directly from web contents.",
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),
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]
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DEFAULT_CONFIG_NAME = "canonical"
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def _info(self):
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if self.config.name == "canonical":
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features = datasets.Features(
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{
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"arg1": datasets.Value("string"),
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"rel": datasets.Value("string"),
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"arg2": datasets.Value("string"),
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"support": datasets.Value("int64"),
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"facets": [
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{
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"value": datasets.Value("string"),
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"type": datasets.Value("string"),
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"support": datasets.Value("int64"),
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}
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],
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"source_sentences": [{"text": datasets.Value("string"), "source": datasets.Value("string")}],
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}
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)
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else: # features for the "open" part
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features = datasets.Features(
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{
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"subject": datasets.Value("string"),
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"predicate": datasets.Value("string"),
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"object": datasets.Value("string"),
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"support": datasets.Value("int64"),
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"facets": [
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{
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"value": datasets.Value("string"),
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"type": datasets.Value("string"),
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"support": datasets.Value("int64"),
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}
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],
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"source_sentences": [{"text": datasets.Value("string"), "source": datasets.Value("string")}],
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}
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)
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return datasets.DatasetInfo(
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description=_DESCRIPTION,
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features=features,
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supervised_keys=None,
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homepage=_HOMEPAGE,
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license=_LICENSE,
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citation=_CITATION,
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)
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+
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def _split_generators(self, dl_manager):
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"""Returns SplitGenerators."""
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# my_urls = _URLs[self.config.name]
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# data_file = dl_manager.download_and_extract(my_urls)
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+
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data_file = dl_manager.download_and_extract(_URL)
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+
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return [
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datasets.SplitGenerator(
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name=datasets.Split.TRAIN,
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gen_kwargs={
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"filepath": data_file,
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"split": "train",
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},
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),
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]
|
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+
|
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# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
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def _generate_examples(self, filepath, split):
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"""Yields examples as (key, example) tuples."""
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# This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
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# The `key` is here for legacy reason (tfds) and is not important in itself.
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+
|
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with open(filepath, encoding="utf-8") as f:
|
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for id_, row in enumerate(f):
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data = json.loads(row)
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if self.config.name == "canonical":
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data.pop("subject")
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data.pop("predicate")
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data.pop("object")
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yield id_, data
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else: # "open"
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data.pop("arg1")
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data.pop("rel")
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data.pop("arg2")
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yield id_, data
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dataset_infos.json
ADDED
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{"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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dummy/canonical/1.0.0/dummy_data.zip
ADDED
@@ -0,0 +1,3 @@
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version https://git-lfs.github.com/spec/v1
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oid sha256:7b1f94b3585398b8803d8efaa0311d059f7802c16dc38fd977228344b4ad871b
|
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+
size 1968
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dummy/open/1.0.0/dummy_data.zip
ADDED
@@ -0,0 +1,3 @@
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+
version https://git-lfs.github.com/spec/v1
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+
oid sha256:7b1f94b3585398b8803d8efaa0311d059f7802c16dc38fd977228344b4ad871b
|
3 |
+
size 1968
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