Real-world AI applications, particularly in the Human Resources (HR) domain, demand fairness, factuality, controllability, consistency, interpretability, and reasoning. Our vision is to investigate, innovate, propose, and build symbiotic systems that mutually exploit language models (LM) and knowledge graphs (KG) in a continuous and (semi-) automated learning paradigm to address these challenges. We are tackling many interesting technical problems, from zero-shot methods for extracting knowledge and designing conceptual KGs to learning universal representations in KGs, exploiting symbolic, neural, and hybrid models, and fusing LMs with KGs and vice versa.
We apply our research to build next-generation knowledge graphs for the HR domain that can drive many AI applications. These uses include matching candidates to jobs, developing a deep understanding of skills and occupations, identifying skills gaps, and recommending career opportunities.