JoTA: Aligning Multilingual Job Taxonomies Through Word Embeddings (Student Abstract)

Anna Giabelli, Lorenzo Malandri, Fabio Mercorio, Mario Mezzanzanica

AAAI 2022 pp. 12955-12956

doi:10.1609/AAAI.V36I11.21614 /aaai/2022/giabelli2022aaai-jota/

Abstract

We propose JoTA (Job Taxonomy Alignment), a domain-independent, knowledge-poor method for automatic taxonomy alignment of lexical taxonomies via word embeddings. JoTA associates all the leaf terms of the origin taxonomy to one or many concepts in the destination one, employing a scoring function, which merges the score of a hierarchical method and the score of a classification task. JoTA is developed in the context of an EU Grant aiming at bridging the national taxonomies of EU countries towards the European Skills, Competences, Qualifications and Occupations taxonomy (ESCO) through AI. The method reaches a 0.8 accuracy on recommending top-5 occupations and a wMRR of 0.72.

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Cite

Text

Giabelli et al. "JoTA: Aligning Multilingual Job Taxonomies Through Word Embeddings (Student Abstract)." AAAI Conference on Artificial Intelligence, 2022. doi:10.1609/AAAI.V36I11.21614

Markdown

[Giabelli et al. "JoTA: Aligning Multilingual Job Taxonomies Through Word Embeddings (Student Abstract)." AAAI Conference on Artificial Intelligence, 2022.](https://mlanthology.org/aaai/2022/giabelli2022aaai-jota/) doi:10.1609/AAAI.V36I11.21614

BibTeX

@inproceedings{giabelli2022aaai-jota,
  title     = {{JoTA: Aligning Multilingual Job Taxonomies Through Word Embeddings (Student Abstract)}},
  author    = {Giabelli, Anna and Malandri, Lorenzo and Mercorio, Fabio and Mezzanzanica, Mario},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2022},
  pages     = {12955-12956},
  doi       = {10.1609/AAAI.V36I11.21614},
  url       = {https://mlanthology.org/aaai/2022/giabelli2022aaai-jota/}
}