Building Knowledge Intensive Architectures for Heterogeneous NLP Workflows

Abstract

Workflows are core part of every modern organization ensuring smooth running operations, task consistency and process automation. Dynamic workflows are being used increasingly due to their flexibility in a working environment where they minimize mundane tasks like long-term maintenance and increase productivity by automatically responding to changes and introducing new processes. Constant changes within unstable environments where information may be sparse, inconsistent and uncertain can create a bottleneck to a workflow in predicting behaviours effectively. Within a business environment, automatic applications like customer support, complex incidents can be regarded as instances of a dynamic process since mitigation policies have to be responsive and adequate to any case no matter its unique nature. Support engineers work with any means at their disposal to solve any emerging case and define a custom prioritization strategy, to achieve the best possible result. This paper describes a novel workflow architecture for heavy knowledge-related application workflows to address the tasks of high solution accuracy and shorter prediction resolution time. We describe how policies can be generated against cases deriving from heterogeneous workflows to assist experts and domain-specific reusable cases can be generated for similar problems. Our work is evaluated using data from real business process workflows across a large number of different cases and working environments.