Enriching employee ontology for enterprises with knowledge discovery from social networks

  • Authors:

  • Hao Wu;Charalampos Chelmis;Vikram Sorathia;Yinuo Zhang;Om Prasad Patri;Viktor K. Prasanna

  • Affiliations:

  • University of Southern California;University of Southern California;University of Southern California;University of Southern California;University of Southern California;University of Southern California

  • Venue:
  • Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
  • Year:
  • 2013

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Abstract

To enhance human resource management and personalized information acquisition, employee ontology is used to model business concepts and relations between them for enterprises. In this paper, we propose an employee ontology that integrates user static properties from formal structures with dynamic interests and expertise extracted from informal communication signals. We mine user's interests at both personal and professional level from informal interactions on communication platforms at the workplace. We show how complex semantic queries enable granular analysis. At the microscopic level, enterprises can utilize the results to better understand how their employees work together to complete tasks or produce innovative ideas, identify experts and influential individuals. At the macroscopic level, conclusions can be drawn, among others, about collective behavior and expertise in varying granularities (i.e. single employee to the company as a whole).