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.
@article{arxiv.2310.05258,
title = {A Knowledge Graph-Based Search Engine for Robustly Finding Doctors and Locations in the Healthcare Domain},
author = {Mayank Kejriwal and Hamid Haidarian and Min-Hsueh Chiu and Andy Xiang and Deep Shrestha and Faizan Javed},
journal= {arXiv preprint arXiv:2310.05258},
year = {2023}
}
Comments
Presented as an applied data science poster in KDD 2023