[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121732-en":3,"doc-seo-121732-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121732,34359740700684,"Finn","https://ap-avatar.wpscdn.com/avatar/1f400023980c374ae676?_k=1777273430885731487",8,"Research & Report","Research on Risk Prediction and Early Warning of Human Resource Management Based on Machine Learning and Ontology Reasoning","Talent management risk prediction and early warning for enterprises rely on retaining key employees, supported by machine learning and ontological reasoning. The approach builds a case library and assesses similarity using concept-name and attribute similarity, enabling knowledge-based job matching for candidate evaluation under specific risk scenarios. A prediction model for early-warning is developed, optimized with cross-validation and regularization to accelerate convergence. Results reach 82% yield and demonstrate improved risk control through analysis and suggested mitigation measures.","ISSN 1330-3651 (Print), ISSN 1848-6339 (Online) [https://doi.org/10.17559/TV-20230810000869](https://doi.org/10.17559/TV-20230810000869)  \nPreliminary communication  \nResearch on Risk Prediction and Early Warning of Human Resource Management Based  \non Machine Learning and Ontology Reasoning  \nMiaomiao TANG*, Tianwu ZHAO, Zhongdan HU, Qinghua LI  \nAbstract: Talent is the first resource, the development of the enterprise to retain key talent is essential, the main research is based on machine learning and ontological reasoning, human resources analysis and management risk prediction and early warning methods, first of all, according to the specific situation and the target case, through the calculation of the similarity of the concept name and attribute of the similarity assessment of the source case in the case library, the matching of knowledge-based employees of the company's case for the similarity prediction and human resources management risk prediction research. Then, according to the evaluation results, we can find out the most suitable job matches in specific risk problems and situations. This is a solution to the target cases and criteria for companies to evaluate candidates. Second, we have successfully developed and implemented a prediction model that applies machine learning to the early warning study of risk prediction for HR management. The model is optimized with a cross-validation function, and the convergence of the model training is accelerated by the regularization of Newton's iterative method. Finally, our prediction model achieved 82% yield. Ontological reasoning and machine learning are promising in human resource management risk prediction and warning, which is proved by the high accuracy rate verified by examples. Finally, we analyze the proposed results of HRM risk prediction and early warning to contribute to the improvement of risk control and suggest measures for possible risks.  \nKeywords: human resource management; machine learning; ontology reasoning; regularization; risk prediction  \n1 INTRODUCTION  \nEnterprise strategy has become a part of the enterprise strategic management, human resource management of the enterprise to obtain the maximum effectiveness of the talent important impact, relying on technological innovation and talent development, and ultimately realize the development of human resources of large enterprises. The 21st century is the era of knowledge-based economy, digital economy, and competition for talent, and the only way for enterprises to occupy a favorable position in the fierce competition is to have a strong and mature human resource management system. In the face of such a severe situation, enterprises have increased the participation of human resource management, and improved the human resource risk outlook and early warning from all aspects. If enterprises want to improve the level of risk prediction and early warning of human resource management, they should start from limiting the risk, take the initiative to find and excavate the danger, take appropriate measures to limit the danger, and should maximize the level of human resources. Resource Management Risk Prediction and Early Warning [2] . In view of the traditional personnel management risk prediction and early warning system cannot meet the needs of the enterprise big data in order to make the system work properly, the current need for massive data manual screening and processing to reduce the amount of big data, but this processing seems to return to the era of the old data, seriously hindering the improvement of the efficiency of the work of human resource management risk prediction; and early warning system (HR) [3] plays an important role, and some people have proposed the use of Data Mining [4] to reduce the data that need to be processed, and the purpose of Data Mining is to analyze and discover the data patterns of the massive raw data, and ultimately transform the raw data into useful information and know","cbCaippsaE7178GD","https://ap.wps.com/l/cbCaippsaE7178GD","pdf",997173,1,10,"English","en",105,"# Introduction\n# Related Work","[{\"question\":\"How does the method predict HR management risks and provide early warning?\",\"answer\":\"It combines machine learning with ontology reasoning to evaluate case similarity, then uses the evaluation results to support job matching for specific risk problems and situations.\"},{\"question\":\"What is the role of the case library in this research?\",\"answer\":\"The case library stores employee knowledge and case attributes; similarity between concept names and attributes is computed to enable matching and risk-related prediction.\"},{\"question\":\"How is the prediction model optimized and how effective is it?\",\"answer\":\"The model uses cross-validation and regularization based on Newton’s iterative method to accelerate training convergence; it achieves 82% yield in the reported evaluation.\"}]","Research on Risk Prediction and Early Warning of Human Resource Management Based on Machine Learning and Ontology Reasoning | PDF",1785806543,25,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"research-on-risk-prediction-and-early-warning-of-human-resource-management-based-on-machine-learning-and-ontology-reasoning","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/research-on-risk-prediction-and-early-warning-of-human-resource-management-based-on-machine-learning-and-ontology-reasoning/121732/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"How does the method predict HR management risks and provide early warning?","Question",{"text":75,"@type":76},"It combines machine learning with ontology reasoning to evaluate case similarity, then uses the evaluation results to support job matching for specific risk problems and situations.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What is the role of the case library in this research?",{"text":80,"@type":76},"The case library stores employee knowledge and case attributes; similarity between concept names and attributes is computed to enable matching and risk-related prediction.",{"name":82,"@type":73,"acceptedAnswer":83},"How is the prediction model optimized and how effective is it?",{"text":84,"@type":76},"The model uses cross-validation and regularization based on Newton’s iterative method to accelerate training convergence; it achieves 82% yield in the reported evaluation.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":21,"slug":133},"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]