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Taxonomy of concepts for AI improvement, based on matrices of concepts

integration_instructionsResearch softwarekeyboard_double_arrow_right Software 05 Mar 2023Publisher:Zenodo
Authors: Franceschi, Paul;
Code repository: https://github.com/paulfranceschi/taxonomy-of-concepts-for-AI/tree/v1.0.1

doi: 10.5281/zenodo.7708645 , 10.5281/zenodo.7725343 , 10.5281/zenodo.7769078 , 10.5281/zenodo.7699767 , 10.5281/zenodo.7699766 , 10.5281/zenodo.7712672

Taxonomy of concepts for AI improvement, based on matrices of concepts

Abstract

The aim of this project is to create a library in Python based on a taxonomy of commonly used concepts, so that it can be used for artificial intelligence (AI) improvement.

If you use this software, please cite it using the metadata from this file.

Keywords

python, common concepts, taxonomy, concepts, corpus generator, natural language processing, citation generator, natural language generation

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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