SBIR Phase I: A Semantic Data-Driven Human Capital Recommendation System

This SBIR Phase I project proposes to apply the combination of semantic matchmaking and a knowledge acquisition loop to human capital selection processes to generate a rank-ordered short-list of qualified candidates. The problem with available job matching methods is they rely primarily on keyword searches and lack comprehensive learning that includes feedback on quality of recommendations. The opportunity is to build technology to create a candidate recommendation system incorporating feedback on relevance of its performance. The research objective is to determine if integrating semantic matching and machine learning provides a feasible basis for creating a commercially viable predictive candidate recommendation system. The natural language processing techniques, and the ontology knowledge base developed during this research, have the potential to advance knowledge and understanding of using natural text to express the availability of skills and experience. The anticipated result is the creation of a human capital ontology structure and rules that learn from feedback loops of rank-order successes to improve semantic matching. While this project assesses the significance of ontology and machine learning to improve the quality of candidate recommendations, the results also enhance technological understanding of combining ontology and machine learning within broader work-force science research. The broader/commercial impact of this product is to significantly increase the success of selected applicants and improve employers' ability to manage human capital selection efficiently and at less cost by presenting them with a more accurate short-list. Identifying the most qualified candidates should increase the return on investment in over $6 billion spent annually by businesses in the United States for online recruiting efforts ultimately resulting in greater organizational competence and competitive advantage. By implementing technology that automates the process of rank-ordering job candidates based on skills, qualifications, and machine-learning from historical selections, businesses will be able to reduce costs associated with identifying quality job candidates while dealing with the growing challenge of talent shortages, especially in Science, Technology, Engineering, and Math (STEM) professions. The feedback loop provides for knowledge acquisition to be integrated; thus improving results over time and reducing effort needed to identify qualified candidates. Such insights into talent data will help businesses make more informed recruiting, hiring, and other resource management decisions. This innovation has the potential to extend to talent information in disparate sources offering Big Data analytics solutions for many talent management functions and providing unprecedented insights for human capital management.