Knowledge Base 4.0: using crowdsourcing services for mimicking the knowledge of domain experts

Intelligence is the ability to learn from experience. Knowledge workers in knowledge-intensive processes develop invaluable domain-specific expertise and knowledge over time. Accordingly, it is vital for organizations to capture this knowledge (which is hidden in the biological Neural Network of subject-matter experts) and enable novices/inexperienced knowledge workers to benefit from that in choosing the best next steps. This position paper proposes linking weak supervision and crowd-sourcing techniques to incorporate knowledge in a continual fashion based on estimating uncertainty or errors from the existing knowledge and learning models. Applications may include handling cold start and concept drift situations. We discuss the design of an intelligent Knowledge Base (KB), namely KB 4.0, for mimicking the knowledge of domain experts in knowledge-intensive processes, and using this knowledge to facilitate auto labelling of the data to be used in learning algorithms. We present a motivating scenario in police investigation processes, and argue how inexperienced investigators can benefit from such a domain-specific KB in law enforcement.