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

Authors

Anna Giabelli

Department of Informatics, Systems and Communication, University of Milano-Bicoca, Milan, Italy CRISP Research Centre, Univ. of Milano-Bicocca, Milan, Italy


Lorenzo Malandri

Department of Statistics and Quantitative Methods, University of Milano-Bicocca, Milan, Italy CRISP Research Centre, Univ. of Milano-Bicocca, Milan, Italy


Fabio Mercorio

Department of Statistics and Quantitative Methods, University of Milano-Bicocca, Milan, Italy CRISP Research Centre, Univ. of Milano-Bicocca, Milan, Italy


Mario Mezzanzanica

Department of Statistics and Quantitative Methods, University of Milano-Bicocca, Milan, Italy CRISP Research Centre, Univ. of Milano-Bicocca, Milan, Italy


Proceedings:

No. 11: IAAI-22, EAAI-22, AAAI-22 Special Programs and Special Track, Student Papers and Demonstrations

Volume

Issue:

Proceedings of the AAAI Conference on Artificial Intelligence, 36

Track:

AAAI Student Abstract and Poster Program

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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.

DOI:

10.1609/aaai.v36i11.21614


AAAI

Proceedings of the AAAI Conference on Artificial Intelligence, 36