Creating and evolving knowledge graphs at scale for explainable AI - Safe & Trusted AI

Knowledge graphs and knowledge bases are forms of symbolic knowledge representations used across AI applications. Both refer to a set of technologies that organise data for easier access, capture information about people, places, events, and other entities of interest, and forge connections between them. As AI (re-)conquered the world, symbolic knowledge representations became ubiquitous, and are now extensively used in everything from search engines and chatbots to product recommenders and autonomous systems, especially in the context of neuro-symbolic approaches. Knowledge engineering is the field that encompasses technical and social aspects related to building knowledge-based AI systems. In its most recent manifestation, it involves complex, human-machine workflows including knowledge acqusition from experts, crowdsourced entity typing and reconciliation, argumentation and discussion support, information extraction algorithms across different data modalities, and database lifting.