Creating Dynamically Evolving Ontologies: A Use Case from the Labour Market Domain

conference paper
2023
Boer, M.H.T. De

Bakker, R.M.

Burghoorn, M.
The world is changing, which means that formal representations of (part of) the world should change with it. In this paper, we explore to what extent automation of updating ontologies or taxonomies could be possible using Hybrid AI. We use Natural Language Processing (NLP) methods to automatically recognize and integrate new concepts and alternative labels in an ontology. The labour market domain is used as a use case, as new jobs and skills should be added on a regular basis. In our experiments we show that with our dataset 1) language-based methods seem to outperform a string-based method, but no clear difference between language-based methods can be observed; 2) it is easier to map skills within one ontology (to alternative labels / synonyms) compared to between different sources; 3) no clear difference in performance between mapping with synonyms / more relevant text compared to without is visible yet. This means that we can certainly take steps towards automation in the field of ontology evolution, but we are not there yet. In the future we plan to further experiment with at least the integration of skills (3), as well as the creation of a human-in-the-loop system to validate our work and to combine the strengths of humans and machines. (C) 2023 CEUR-WS. All rights reserved.
Topics
Natural Language ProcessingOntology EvolutionOntology MappingTransformerCommerceEmploymentMappingNatural language processing systemsFormal representationsLabour marketLanguage processingNatural language processingNatural languagesOntology evolutionOntology mappingOntology'sProcessing methodTransformerOntology
TNO Identifier
987960
Repository link
https://resolver.tno.nl/uuid:40568987-a9ae-4d9a-a1dc-ee30564d5e3c
ISSN
16130073
Publisher
CEUR-WS
Source title
CEUR Workshop Proceedings
Editor(s)
Martin, A.
Fill, H.G.
Gerber, A.
Hinkelmann, K.
Lenat, D.
Stolle, R. van
Harmelen, F. van
Files
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