Conversational BI: an ontology-driven conversation system for business intelligence applications

Abstract

Business intelligence (BI) applications play an important role in the enterprise to make critical business decisions. Conversational interfaces enable non-technical enterprise users to explore their data, democratizing access to data significantly. In this paper, we describe an ontology-based framework for creating a conversation system for BI applications termed as Conversational BI. We create an ontology from a business model underlying the BI application, and use this ontology to automatically generate various artifacts of the conversation system. These include the intents, entities, as well as the training samples for each intent. Our approach builds upon our earlier work, and exploits common BI access patterns to generate intents, their training examples and adapt the dialog structure to support typical BI operations. We have implemented our techniques in Health Insights (HI), an IBM Watson Healthcare offering, providing analysis over insurance data on claims. Our user study demonstrates that our system is quite intuitive for gaining business insights from data. We also show that our approach not only captures the analysis available in the fixed application dashboards, but also enables new queries and explorations.