Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Proceedings - 16th IEEE International Conference on Semantic Computing, ICSC 2022 |
| Herausgeber (Verlag) | Institute of Electrical and Electronics Engineers Inc. |
| Seiten | 195-200 |
| Seitenumfang | 6 |
| ISBN (elektronisch) | 9781665434188 |
| Publikationsstatus | Veröffentlicht - 2022 |
| Extern publiziert | Ja |
| Veranstaltung | 16th IEEE International Conference on Semantic Computing, ICSC 2022 - Virtual, Online, USA / Vereinigte Staaten Dauer: 26 Jan. 2022 → 28 Jan. 2022 |
Publikationsreihe
| Name | Proceedings - 16th IEEE International Conference on Semantic Computing, ICSC 2022 |
|---|
Abstract
Due to the rapidly changing labor market and the consequently widening information gap between the labor market and education, there is a need for methods that can tackle, or at least ease, the construction of labor market ontologies. The current study set out to examine the viability of Ontology Learning (OL) methods for the (semi-)automated construction of labor market ontologies and / or taxonomies. The purpose of this paper is to propose an unsupervised framework, OntoJob, that can identify and extract from raw vacancy text instances, attributes, and relations, such as job titles, worker qualities, and the non-Taxonomic 'is-A' relations between those concepts, and convert those to an expressive descriptive logic. Evaluation of the extracted worker qualities from OntoJob, using a small sample of 5621 job postings representing 1048 occupations, showed an overall lexical precision of 0.36 and recall of 0.22.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Artificial intelligence
- Informatik (insg.)
- Computernetzwerke und -kommunikation
- Informatik (insg.)
- Angewandte Informatik
- Entscheidungswissenschaften (insg.)
- Informationssysteme und -management
Zitieren
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- BibTex
- RIS
Proceedings - 16th IEEE International Conference on Semantic Computing, ICSC 2022. Institute of Electrical and Electronics Engineers Inc., 2022. S. 195-200 (Proceedings - 16th IEEE International Conference on Semantic Computing, ICSC 2022).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - OntoJob
T2 - 16th IEEE International Conference on Semantic Computing, ICSC 2022
AU - Vrolijk, Jarno
AU - Mol, Stefan T.
AU - Weber, C.
AU - Tavakoli, Mohammadreza
AU - Kismihok, Gabor
AU - Pelucchi, Mauro
PY - 2022
Y1 - 2022
N2 - Due to the rapidly changing labor market and the consequently widening information gap between the labor market and education, there is a need for methods that can tackle, or at least ease, the construction of labor market ontologies. The current study set out to examine the viability of Ontology Learning (OL) methods for the (semi-)automated construction of labor market ontologies and / or taxonomies. The purpose of this paper is to propose an unsupervised framework, OntoJob, that can identify and extract from raw vacancy text instances, attributes, and relations, such as job titles, worker qualities, and the non-Taxonomic 'is-A' relations between those concepts, and convert those to an expressive descriptive logic. Evaluation of the extracted worker qualities from OntoJob, using a small sample of 5621 job postings representing 1048 occupations, showed an overall lexical precision of 0.36 and recall of 0.22.
AB - Due to the rapidly changing labor market and the consequently widening information gap between the labor market and education, there is a need for methods that can tackle, or at least ease, the construction of labor market ontologies. The current study set out to examine the viability of Ontology Learning (OL) methods for the (semi-)automated construction of labor market ontologies and / or taxonomies. The purpose of this paper is to propose an unsupervised framework, OntoJob, that can identify and extract from raw vacancy text instances, attributes, and relations, such as job titles, worker qualities, and the non-Taxonomic 'is-A' relations between those concepts, and convert those to an expressive descriptive logic. Evaluation of the extracted worker qualities from OntoJob, using a small sample of 5621 job postings representing 1048 occupations, showed an overall lexical precision of 0.36 and recall of 0.22.
KW - labor market intelligence
KW - ontology engineering
KW - ontology learning
UR - http://www.scopus.com/inward/record.url?scp=85127584352&partnerID=8YFLogxK
U2 - 10.1109/ICSC52841.2022.00040
DO - 10.1109/ICSC52841.2022.00040
M3 - Conference contribution
AN - SCOPUS:85127584352
T3 - Proceedings - 16th IEEE International Conference on Semantic Computing, ICSC 2022
SP - 195
EP - 200
BT - Proceedings - 16th IEEE International Conference on Semantic Computing, ICSC 2022
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 26 January 2022 through 28 January 2022
ER -