5. Discussion
Knowledge-driven recommender systems base their suggestions on formalized background knowledge rather than user behavior. They do not rely upon behavioral data and are, therefore, not affected by the “cold start” problem of collaborative approaches.
The research presented in this paper introduces
a knowledge graph construction method used for creating a real-time continuing education knowledge graph that summarizes knowledge extracted from education provider websites; and
a recommender system that draws upon an occupation knowledge base and the extracted knowledge on continuing education for suggesting career paths and education facilitating them.
Our evaluation of the knowledge graph construction method focused on five tasks performed by the knowledge extraction components (page segment recognition, page segment classification, entity recognition, entity classification, and entity linking), and the overall slot-filling task. The evaluation identified the content classification component, the recall of the entity classification task, and the disambiguation algorithm deployed for entity linking as major areas for improvement. Since slot filling relies upon the outcome of the preceding methods, addressing these shortcomings would also improve its overall performance.
The automatic knowledge graph construction component has been deployed to a corpus of 97,142 educations yielding a continuing education graph that comprises 73,969 nodes and 734,447 edges. The created knowledge graph has been applied to multiple industrial settings in which the reported error types do not significantly impact usefulness. External industrial stakeholders use the continuing education graph for applications such as enabling semantic search across education programs and data analytics. Stakeholder interviews which included experts and the senior management from three different companies that use the continuing education graph in their products revealed that stakeholders rated the data quality either as satisfactory (4 out of 6 points) or good (5 out of 6 points). The external stakeholders acknowledged the potential of the automatically created continuing education knowledge graph and see clear benefits from its current use within their businesses.
One reason for the positive stakeholder assessment lies within the distribution of continuing education offerings across the analyzed websites. Large education providers not only tend to correctly implement web standards (which improves the accuracy of the introduced content extraction components) but also contribute most of the relevant education. The CareerCoach 2022 gold standard used in
Section 4.1, in contrast, has been designed with source variety in mind and, therefore, underestimated the system’s performance in a real-world setting.
The evaluation of the recommender system outlined in
Section 3.2 also leverages the created continuing education knowledge graph in conjunction with the ×28 occupation knowledge base. For use cases that require higher levels of precision and recall, the system could be deployed as part of a semi-automatic knowledge graph-building process that increases efficiency and effectiveness by providing domain experts with suggestions for integrating new concepts and relations into the knowledge graph.
The evaluation of the recommender system aimed at (i) obtaining information on the system’s performance for career path and education suggestions, (ii) outlining the method’s potential, and (iii) collecting information on how weaknesses of the continuing education knowledge graph impact the system’s performance.
Literature indicates, that the sophistication of the employees’ current occupation considerably impacts their willingness to participate in reskilling and upskilling activities. Highly-skilled workers are considerably more likely to partake in continuing education than employees that work in occupations that require little or no formal vocational education and a lot of routine tasks [
59]. Our experiments aimed at considering these different employee segments by benchmarking system performance for occupations with different educational requirements.
The career path recommender suggests career paths based on the industry partner’s occupation knowledge base. An in-depth analysis of its performance revealed that it worked particularly well if closely related target occupations exist. In cases where such closely related occupations have not been available, the quality of its recommendations deteriorated, but so does the agreement between experts, which provided the gold standard for ranking the recommender.
Evaluation results for the education recommender, in contrast, have been strongly affected by the occupation’s coverage in the continuing education ontology. In addition, the recommendation task has been significantly more difficult, which is also reflected in a low expert agreement (moderate agreement for an education’s beneficialness and only slight agreement for its sufficiency). The evaluation yielded the following key insights:
suggesting education is a challenging task and even experts struggle with providing consistent recommendations. Future work will mitigate this issue by developing strategies for edge cases such as education that only covers parts of the relevant skills.
one of the system’s biggest strengths, the availability of real-time information on online courses and educational offerings that have been directly obtained from the provider’s websites, also became its major weakness, since education that has not been covered in the input sources are not considered. In Switzerland, crafts, and trades, for example, are taught through apprenticeships. Consequently, the coverage of continuing education for crafts and trades has been insufficient within the continuing education ontology forcing us to remove a total of eight career paths from the evaluation. In addition, the career path to SAP business analyst had to be discarded, since no suitable education had been available in the knowledge graph.
the system does not yet consider the efforts required for completing further education. Consequently, it preferred more comprehensive education over quicker ones. Edge cases demonstrating this problem have been career paths where on-the-job experience could have been sufficient for advancing to a more prestigious occupation (e.g., from office assistant to office manager). Although all the system’s recommendations have been suitable and would have been beneficial towards a possible promotion, domain experts did not see a requirement for further education.
Author Contributions
Conceptualization, A.W.; methodology, A.W., A.F., R.W., A.v.S. and P.K.; software, R.W., A.F., A.v.S. and P.K.; validation, R.W., A.F. and P.K.; formal analysis, A.W.; resources, R.W. and A.v.S.; data curation, R.W. and A.v.S.; writing—original draft preparation, A.W., R.W., A.F., A.v.S. and P.K.; writing—review and editing, A.W., R.W., A.F., A.v.S. and P.K.; visualization, A.W., R.W., P.K. and A.F.; supervision, A.W.; project administration, A.W.; funding acquisition, A.W. All authors have read and agreed to the published version of the manuscript.
Funding
The research presented in this paper has been conducted within the CareerCoach project (
www.fhgr.ch/CareerCoach, accessed on 17 October 2022) which is funded by Innosuisse under grant number 48713.1 IP-ICT.
Institutional Review Board Statement
Not applicable.
Informed Consent Statement
Not applicable.
Data Availability Statement
Acknowledgments
The authors would like to thank Cornel Müller and Matthias Hewelt for their support in acquiring and performing the CareerCoach project.
Conflicts of Interest
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
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