A BFS-based pruning algorithm for disease-symptom knowledge graph database

Abstract

Breadth-first search (BFS) is one of the most fundamental algorithms for searching a graph. In our previous work, a mobile-assisted diagnosis scheme has been proposed and we have designed a Disease-Symptom data model as a large knowledge base. In this work, we present the design and implementation of a pruning algorithm to capture a part of the large graph-based data model. Generally, to search a large graph, most of the graph queries are too long and very cumbersome to write and sometimes very difficult to implement. Pruning algorithm is one of the prominent solutions of this problem. It results a subgraph or forest for the desired input. Here, our proposed pruning algorithm is multi-source sequential BFS algorithm and it is demonstrated on Disease-Symptom graph database.