Role of Semantic web in the changing context of Enterprise Collaboration

In order to compete with the global giants, enterprises are concentrating on their core competencies and collaborating with organizations that compliment their skills and core activities. The current trend is to develop temporary alliances of independent enterprises, in which companies can come together to share skills, core competencies and resources. However, knowledge sharing and communication among multidiscipline companies is a complex and challenging problem. In a collaborative environment, the meaning of knowledge is drastically affected by the context in which it is viewed and interpreted; thus necessitating the treatment of structure as well as semantics of the data stored in enterprise repositories. Keeping the present market and technological scenario in mind, this research aims to propose tools and techniques that can enable companies to assimilate distributed information resources and achieve their business goals

A Comprehensive Survey of Artificial Intelligence Techniques for Talent Analytics

In today's competitive and fast-evolving business environment, it is a critical time for organizations to rethink how to make talent-related decisions in a quantitative manner. Indeed, the recent development of Big Data and Artificial Intelligence (AI) techniques have revolutionized human resource management. The availability of large-scale talent and management-related data provides unparalleled opportunities for business leaders to comprehend organizational behaviors and gain tangible knowledge from a data science perspective, which in turn delivers intelligence for real-time decision-making and effective talent management at work for their organizations. In the last decade, talent analytics has emerged as a promising field in applied data science for human resource management, garnering significant attention from AI communities and inspiring numerous research efforts. To this end, we present an up-to-date and comprehensive survey on AI technologies used for talent analytics in the field of human resource management. Specifically, we first provide the background knowledge of talent analytics and categorize various pertinent data. Subsequently, we offer a comprehensive taxonomy of relevant research efforts, categorized based on three distinct application-driven scenarios: talent management, organization management, and labor market analysis. In conclusion, we summarize the open challenges and potential prospects for future research directions in the domain of AI-driven talent analytics.Comment: 30 pages, 15 figure