A Weighted Heterogeneous Graph Based Dialogue System

TL;DR

This paper introduces a weighted heterogeneous graph and a Graph-DQN model for disease diagnosis dialogue systems, improving relation understanding and reducing dialogue turns compared to existing methods.

Contribution

It presents a novel weighted heterogeneous graph construction and a Graph-DQN approach that enhances symptom-disease relation modeling in dialogue systems.

Findings

Outperforms state-of-the-art models in disease diagnosis tasks.

Completes diagnosis with fewer dialogue turns.

Better distinguishes diseases with similar symptoms.

Abstract

Knowledge based dialogue systems have attracted increasing research interest in diverse applications. However, for disease diagnosis, the widely used knowledge graph is hard to represent the symptom-symptom relations and symptom-disease relations since the edges of traditional knowledge graph are unweighted. Most research on disease diagnosis dialogue systems highly rely on data-driven methods and statistical features, lacking profound comprehension of symptom-disease relations and symptom-symptom relations. To tackle this issue, this work presents a weighted heterogeneous graph based dialogue system for disease diagnosis. Specifically, we build a weighted heterogeneous graph based on symptom co-occurrence and a proposed symptom frequency-inverse disease frequency. Then this work proposes a graph based deep Q-network (Graph-DQN) for dialogue management. By combining Graph Convolutional Network (GCN) with DQN to learn the embeddings of diseases and symptoms from both the structural and attribute information in the weighted heterogeneous graph, Graph-DQN could capture the symptom-disease relations and symptom-symptom relations better. Experimental results show that the proposed dialogue system rivals the state-of-the-art models. More importantly, the proposed dialogue system can complete the task with less dialogue turns and possess a better distinguishing capability on diseases with similar symptoms.