Abstract
The purpose of this study was to define a data science architecture for talent acquisition. The approach was to propose analytics that derive data. The originality of this paper consists in proposing an architecture to work within the process of obtaining semantically enriched data by using data science and Semantic Web technologies. We applied the proposed architecture and developed a case study-based prototype that uses analytics techniques for résumé data integrated with Linked Data technologies. We conducted a case study to identify skills by applying classification via regression, k-nearest neighbors (k-NN), random forest, naïve Bayes, support vector machine, and decision tree algorithms to résumé data that we previously described with terms from publicly available ontologies. We labeled data from résumés using terms from existing human resource ontologies. The main contribution is the extraction of skills from résumés and the mining of data that was previously described with the Semantic Web.
Highlights
People analytics is fast becoming a key instrument in talent management
There is limited literature in human resource analytics to guide the use of machine learning algorithms [7]
We proposed a Semantic Web data science architecture and validated it on résumés described with the Semantic Web
Summary
People analytics is fast becoming a key instrument in talent management. Human resource analytics, called talent analytics, is the application of considerable data mining and business analytics techniques to human resources data [1,2]. There is limited literature in human resource analytics to guide the use of machine learning algorithms [7]. Examples of the use of analytics in talent management are data mining (extracting and examining data from large databases), sentiment analysis, and controlled tests such as A/B testing [10]. We proposed a Semantic Web data science architecture and validated it on résumés described with the Semantic Web. Srivastava et al [11] provided several predictive analytics to address talent acquisition needs such as predicting joining delay, selection likelihood, and offer acceptance likelihood. Aldarra and Munoz [22] applied J48 algorithm to construct a Linked Data-based decision tree classifier to review movies They used the SPARQL Protocol and RDF Query Language (SPARQL) queries to derive features. Thehestsrturcutcutrueroefotfhtihs ipsappaepreirs iass faosllofowllso:wSesc: tiSoenct2io, Mn a2t,erMiaalstearniadlsMaetnhdodMs,epthreosdesn,tsptrheeserenstesartchhe mreestheaordcohlomgye,tRheosdooulrocgeyD, eRsecrsioputirocne DFreasmcreipwtoiornk (FRraDmFe) wknoorkwl(eRdDgFe)bkanseowcolendsgtreucbtaiosen,caonndstrfeuacttuiorne,eanngdinefeeartiunrge, Seencgtiinoene3rindgis,cSuescsteisonth3e dreissucultsss,easntdheSercetsiuolnts, apnredsSenectstiothne4dpisrceussesnitosntsh.e discussions
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