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With the development of data mining technology, diverse and broader domain knowledge can be extracted automatically. However, the research on applying knowledge mapping and data visualization techniques to genealogical data is limited. This paper aims to fill this research gap by providing a systematic framework and process guidance for practitioners seeking to uncover hidden knowledge from genealogy.

Based on a literature review of genealogy's current knowledge reasoning research, the authors constructed an integrated framework for knowledge inference and visualization application using a knowledge graph. Additionally, the authors applied this framework in a case study using “Manchu Clan Genealogy” as the data source.

The case study shows that the proposed framework can effectively decompose and reconstruct genealogy. It demonstrates the reasoning, discovery, and web visualization application process of implicit information in genealogy. It enhances the effective utilization of Manchu genealogy resources by highlighting the intricate relationships among people, places, and time entities.

This study proposed a framework for genealogy knowledge reasoning and visual analysis utilizing a knowledge graph, including five dimensions: the target layer, the resource layer, the data layer, the inference layer, and the application layer. It helps to gather the scattered genealogy information and establish a data network with semantic correlations while establishing reasoning rules to enable inference discovery and visualization of hidden relationships.

This paper aims to build a moderate mediation model to delineate when and how employee with perceived overqualification will exert extra effort and therefore engage in more altruistic helping behavior.

The research hypotheses were empirically tested using multitime and multisource survey data. Given the nested nature of data (i.e. 52 immediate supervisors rated 143 subordinates), multilevel structural equation modeling analyses within Mplus were conducted to test the proposed model.

The results support the proposed moderated mediation effect and indicate that perceived overqualification is positively related to extra effort on a condition that there is either strong desire for higher workplace status or more developmental job opportunities. The extra effort will subsequently lead to more altruistic helping behavior.

Based on the findings of this paper, human resource managers should consider the job applicant’s desire for workplace status and the organizational context the employer can provide when hiring overqualified employees. Second, organizations should carefully conduct job design to improve overqualified employees’ on-the-job developmental experiences. Third, training programs should be conducted to help satisfy needs and improve workplace status of overqualified employees, so that they can exert extra job effort and engage in pro-organizational behaviors.

Drawing on motivation–opportunity–ability theory, this paper extends the limited understanding of important boundary conditions under which perceived overqualification can be beneficial. The findings add to the knowledge on extant literature by identifying altruistic helping behavior as a new outcome of perceived overqualification.