A Knowledge Graph-Based Search Engine for Robustly Finding Doctors and Locations in the Healthcare Domain

TL;DR

This paper introduces a knowledge graph-based search engine designed to improve the accuracy and coverage of finding doctors and healthcare locations, outperforming traditional methods especially for complex queries.

Contribution

It presents a novel KG-based architecture tailored for healthcare search, enhancing retrieval robustness and coverage for complex queries.

Findings

Higher coverage for complex queries

Maintains quality while improving search robustness

Effective integration of semantic modeling and structured querying

Abstract

Efficiently finding doctors and locations is an important search problem for patients in the healthcare domain, for which traditional information retrieval methods tend not to work optimally. In the last ten years, knowledge graphs (KGs) have emerged as a powerful way to combine the benefits of gleaning insights from semi-structured data using semantic modeling, natural language processing techniques like information extraction, and robust querying using structured query languages like SPARQL and Cypher. In this short paper, we present a KG-based search engine architecture for robustly finding doctors and locations in the healthcare domain. Early results demonstrate that our approach can lead to significantly higher coverage for complex queries without degrading quality.