datascienceontology
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IBM
Data Science Ontology
Bumps [minimatch](https://github.com/isaacs/minimatch) from 3.0.4 to 3.1.2. Commits 699c459 3.1.2 2f2b5ff fix: trim pattern 25d7c0d 3.1.1 55dda29 fix: treat nocase:true as always having magic 5e1fb8d 3.1.0 f8145c5 Add 'allowWindowsEscape' option 570e8b1...
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Bumps [ajv](https://github.com/ajv-validator/ajv) to 8.12.0 and updates ancestor dependency [ajv-cli](https://github.com/ajv-validator/ajv-cli). These dependencies need to be updated together. Updates `ajv` from 5.5.2 to 8.12.0 Release notes Sourced from ajv's releases. v8.12.0 fix...
Bumps [minimist](https://github.com/minimistjs/minimist) and [mkdirp](https://github.com/isaacs/node-mkdirp). These dependencies needed to be updated together. Updates `minimist` from 1.2.0 to 1.2.8 Changelog Sourced from minimist's changelog. v1.2.8 - 2023-02-09 Merged [Fix] Fix long option...
Closes #25 This PR adds a first attempt at generating OWL ontology artifacts (in OWL/XML, OBO, and OBO Graph JSON) for the Data Science Ontology. It's implemented in a Python...
I have a couple ontologies and controlled vocabularies in mind such as the Intelligent Task Ontology (https://github.com/OpenBioLink/ITO) that might be relevant for alignment (noting that there are already wikidata links...
I'd like to generate an OWL artifact so we can ingest the data science ontology in the ASKEM TA2 domain knowledge graph - would you be willing to accept a...
@epatters wrote in #16: > BTW, for a while I've been considering migrating to a graph database, possibly [Dgraph](https://dgraph.io/), to enable more flexible querying, but I haven't yet been able...
Several preexisting ontologies are worth investigating, both for inspiration and for their contents. In his [PhD thesis](https://www.semanticscholar.org/paper/Understanding-Machine-Learning-Performance-with-van-Vanschoren/e43525a6994150c7a4d39710b4b5750d202d6a2d), Joaquin Vanschoren describes an ontology called [Expose](https://www.openml.org/downloads/expose.owl). The thesis cites a number of...
[ML-Schema](https://arxiv.org/abs/1807.05351) is an upper ontology for machine learning workflows, inspired by [OpenML](https://www.openml.org/)'s data model and created by @joaquinvanschoren and others. We should map the upper-level concepts in the DSO, like...
The ontology should, perhaps, include high-level concepts of data science, such as "data cleaning/preprocessing", "inference", and "evaluation". The usefulness of such concepts is obvious, but there are several difficulties. Unlike...