Gated Tree-based Graph Attention Network (GTGAT) for medical knowledge graph reasoning

Knowledge graph (KG) is a multi-relational data that has proven valuable for many tasks including decision making and semantic search. In this paper, we present GTGAT (Gated Tree-based Graph Attention), a method for tackling the problems of transductive and inductive reasoning in generalized KGs. Based on recent advancement of graph attention network (GAT), we develop a gated tree-based method to distill valuable information in neighborhood via hierarchical-aware and semantic-aware attention mechanism. Our approach not only addresses several key challenges of GAT but is also capable of undertaking multiple downstream tasks. Experimental results have revealed that our proposed GTGAT has matched state-of-the-art approaches across transductive benchmarks on the Cora, Citeseer, and electronic medical record networks (EMRNet). Meanwhile, the inductive experiments on medical knowledge graphs show that GTGAT surpasses the best competing methods for personalized disease diagnosis.

Automated summary

This paper presents GTGAT, a method for addressing transductive and inductive reasoning problems in generalized knowledge graphs. Based on graph attention network (GAT), GTGAT utilizes a gated tree-based approach to extract valuable information. Experimental results demonstrate that GTGAT performs well in both transductive benchmarks and inductive experiments on medical knowledge graphs.