Building a Custom Taxonomy of AI Skills and Tasks from the Ground Up with Job Postings
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Computer Science > Computation and Language
Title:Building a Custom Taxonomy of AI Skills and Tasks from the Ground Up with Job Postings
Abstract:Utilizing LLMs for automated taxonomy construction presents a clear opportunity for the comprehensive, yet efficient mapping of potentially complex domains. When contending with high volumes of rapidly growing corpora, however, it becomes unclear how to best leverage such data for optimal taxonomy construction. Taking the case of systematizing AI skills in the workplace, we use two large-scale job postings corpora to investigate key design decisions for the inclusion (or exclusion) of data points for taxonomy construction. We propose TaxonomyBuilder as a blueprint for our systematic study, with which we evaluate various configurations of custom, data-informed, and hierarchical taxonomies. We demonstrate that less data can provide more clarity: filtering inputs to TaxonomyBuilder provides better domain-specific coverage than offering unfiltered inputs to clustering and LLM-enhanced hierarchical taxonomy labeling tools.
| Comments: | 14 pages, 2 figures, 8 tables. Accepted to CustomNLP4U 2026 |
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2605.21029 [cs.CL] |
| (or arXiv:2605.21029v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2605.21029
arXiv-issued DOI via DataCite (pending registration)
|
Submission history
From: Stephen Meisenbacher [view email][v1] Wed, 20 May 2026 11:01:49 UTC (392 KB)
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