Datasets:
Tasks:
Other
Modalities:
Text
Formats:
parquet
Languages:
English
Size:
10M - 100M
ArXiv:
Tags:
knowledge-base
License:
| 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: 2976665740 | |
| num_examples: 8904060 | |
| download_size: 898478552 | |
| dataset_size: 2976665740 | |
| - 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: 2882646222 | |
| num_examples: 8904060 | |
| download_size: 900156754 | |
| dataset_size: 2882646222 | |
| configs: | |
| - config_name: canonical | |
| data_files: | |
| - split: train | |
| path: canonical/train-* | |
| default: true | |
| - config_name: open | |
| data_files: | |
| - split: train | |
| path: open/train-* | |
| # 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. |