Task Categories: other
Languages: en
Multilinguality: monolingual
Size Categories: 1M<n<10M
Licenses: cc-by-4.0
Language Creators: found
Annotations Creators: found
Source Datasets: original

Dataset Card for Ascent KB

Dataset Summary

This dataset contains 8.9M commonsense assertions extracted by the Ascent pipeline developed at the Max Planck Institute for Informatics. 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 and the website.

Supported Tasks and Leaderboards

The dataset can be used in a wide range of downstream tasks such as commonsense question answering or dialogue systems.


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 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 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:

  "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": ""
      "text": "Elephants are extremely intelligent creatures and an elephant's brain can weigh as much as 4-6 kg.",
      "source": ""

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.


Annotation process


Who are the annotators?


Personal and Sensitive Information


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.

Contact Tuan-Phong Nguyen in case of questions and comments.

Licensing Information

The Creative Commons Attribution 4.0 International License

Citation Information

  title={Advanced Semantics for Commonsense Knowledge Extraction},
  author={Nguyen, Tuan-Phong and Razniewski, Simon and Weikum, Gerhard},
  booktitle={The Web Conference 2021},


Thanks to @phongnt570 for adding this dataset.

Models trained or fine-tuned on ascent_kb

None yet