Driving conversations using structured data

Abstract

We represent dialogue data episodic Knowledge Graphs (eKG), where each incoming interaction is transformed into RDF triples, and the accumulation of conversations over time is stored in a triple store. Each series of interactions produces different eKGs; ergo, we model the dialogue flow as gradual changes to a graph. As each interaction gets incorporated into the eKG, distinct graph patterns arise (referred to as thoughts) used to generate an appropriate response to the incoming information. Choosing the type of thoughts that better guide a conversation and result in a better eKG is a problem that can be modelled as a learning task. We experiment with reinforcement learning, specifically Upper Confidence Bound (UCB), to gradually learn how to improve the state of the eKG. As a reward, we compare different graph semantic and structural measures