Abstract
In this work, we build an ontology (automatically learned) in the domain of Human Ressources by using a simple, efficient and undemanding procedure. Our principal challenge is to tackle the problem of automatically grouping human-provided job titles into a hierarchy and by similarity (as they are presented in human-made HR ontologies). We use the Louvain algorithm, a greedy optimization method that, given a sufficient amount of data, interconnects domain-specific jobs that have more skills in common than jobs from different domains. In our case, we used publicly available profiles from LinkedIn (written in English by users in France). An automatic evaluation was performed and shows that the resulting ontology is similar in size and structure to ESCO (one of the most complete human-made ontology for HR). The whole procedure allows recruitment professionals to easily generate and update this ontology with virtually no human intervention.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Three entities that codify a statement in the form of subject-predicate-object expressions.
- 2.
The hierarchy for the skill/competence and knowledge concepts is not yet available in full.
- 3.
These profiles most probably correspond to LinkedIn users who have not yet any experience in the professional world or who have not updated their profile.
- 4.
Loosely translated as: “head of China market/person who is in charge of the Chinese market”.
- 5.
We did not include a spelling checker because the data contains terminology, acronyms, neologisms and variations that do not correspond to dictionary forms.
- 6.
- 7.
The complete Hola graph is available for a dynamic consultation at http://www-etud.iro.umontreal.ca/~alfonsda/project/holaOntology/index.html.
- 8.
In our case, the classes correspond to the automatically detected communities.
- 9.
Some ontologies from other domains evolve so quickly that conventional classes at the time of the schema conception might become obsolete, e.g., the smartphone sensor network ontologies analyzed in [2], like OntoSensor or CESN have a Class Richness score of 0.59 and 0.71 respectively.
- 10.
Even thought the enrichment of the ESCO ontology was not among this work’s objectives, we do not discard this possible application of the Hola procedure.
References
Alani, H., Brewster, C., Shadbolt, N.: Ranking ontologies with AKTiveRank. In: Cruz, I., et al. (eds.) ISWC 2006. LNCS, vol. 4273, pp. 1–15. Springer, Heidelberg (2006). https://doi.org/10.1007/11926078_1
Ali, S., Khusro, S., Ullah, I., Khan, A., Khan, I.: Smartontosensor: ontology for semantic interpretation of smartphone sensors data for context-aware applications. J. Sens. 2017 (2017)
Blondel, V.D., Guillaume, J.L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. J. Stat. Mech: Theory Exp. 2008(10), P10008 (2008)
Brank, J., Grobelnik, M., Mladenić, D.: A survey of ontology evaluation techniques. In: Slovenian KDD Conference (2005)
Cimiano, P., Völker, J.: Text2Onto. In: Montoyo, A., Muńoz, R., Métais, E. (eds.) NLDB 2005. LNCS, vol. 3513, pp. 227–238. Springer, Heidelberg (2005). https://doi.org/10.1007/11428817_21
Corcho, Ó., Gómez-Pérez, A., González-Cabero, R., Suárez-Figueroa, M.C.: ODEval: a tool for evaluating RDF(S), DAML+OIL, and OWL concept taxonomies. In: Bramer, M., Devedzic, V. (eds.) AIAI 2004. IIFIP, vol. 154, pp. 369–382. Springer, Boston, MA (2004). https://doi.org/10.1007/1-4020-8151-0_32
Dasgupta, S., Padia, A., Maheshwari, G., Trivedi, P., Lehmann, J.: Formal ontology learning from English is-a sentences. arXiv preprint arXiv:1802.03701 (2018)
Dellschaft, K., Staab, S.: On how to perform a gold standard based evaluation of ontology learning. In: Cruz, I., et al. (eds.) ISWC 2006. LNCS, vol. 4273, pp. 228–241. Springer, Heidelberg (2006). https://doi.org/10.1007/11926078_17
Feilmayr, C., Wöß, W.: An analysis of ontologies and their success factors for application to business. Data Knowl. Eng. 101, 1–23 (2016)
Gupta, A., Piccinno, F., Kozhevnikov, M., Pasca, M., Pighin, D.: Revisiting taxonomy induction over wikipedia. In: Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, Osaka, Japan, December 11–17 2016, pp. 2300–2309. EPFL-CONF-227401 (2016)
Kanawati, R.: Détection de communautés dans les grands graphes d’interactions (multiplexes): état de l’art. In: HAL archives ouvertes (2013)
Kessler, R., Lapalme, G.: Agohra: Génération d’une ontologie dans le domaine des ressources humaines. Traitement Automatique des Langues 58(1), 39–62 (2017)
Mukherjee, S., Ajmera, J., Joshi, S.: Unsupervised approach for shallow domain ontology construction from corpus. In: Proceedings of the 23rd International Conference on World Wide Web. WWW 2014 Companion, pp. 349–350. ACM, New York (2014). https://doi.org/10.1145/2567948.2577350
Oliveira, H., Lima, R., Gomes, J., Ferreira, R., Freitas, F., Costa, E.: A confidence–weighted metric for unsupervised ontology population from web texts. In: Liddle, S.W., Schewe, K.-D., Tjoa, A.M., Zhou, X. (eds.) DEXA 2012. LNCS, vol. 7446, pp. 176–190. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-32600-4_14
Posse, C.: Cloud jobs API: machine learning goes to work on job search and discovery (2016). https://cloud.google.com/blog/big-data/2016/11/cloud-jobs-api-machine-learning-goes-to-work-on-job-search-and-discovery
Tartir, S., Arpinar, I.B., Sheth, A.P.: Ontological evaluation and validation. In: Poli, R., Healy, M., Kameas, A. (eds.) Theory and Applications of Ontology: Computer Applications, pp. 115–130. Springer, Dordrecht (2010). https://doi.org/10.1007/978-90-481-8847-5_5
le Vrang, M., Papantoniou, A., Pauwels, E., Fannes, P., Vandensteen, D., De Smedt, J.: ESCO: boosting job matching in europe with semantic interoperability. Computer 47(10), 57–64 (2014)
Wandmacher, T., Ovchinnikova, E., Krumnack, U., Dittmann, H.: Extraction, evaluation and integration of lexical-semantic relations for the automated construction of a lexical ontology. In: Proceedings of the Third Australasian Workshop on Advances in Ontologies, vol. 85, pp. 61–69. Australian Computer Society, Inc. (2007)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Alfonso-Hermelo, D., Langlais, P., Bourg, L. (2019). Automatically Learning a Human-Resource Ontology from Professional Social-Network Data. In: Meurs, MJ., Rudzicz, F. (eds) Advances in Artificial Intelligence. Canadian AI 2019. Lecture Notes in Computer Science(), vol 11489. Springer, Cham. https://doi.org/10.1007/978-3-030-18305-9_11
Download citation
DOI: https://doi.org/10.1007/978-3-030-18305-9_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-18304-2
Online ISBN: 978-3-030-18305-9
eBook Packages: Computer ScienceComputer Science (R0)