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

arXiv: https://arxiv.org/abs/2310.05258 · HTML: https://mlsanity.com/paper/2310.05258/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 19 references that are in the map.
- First posted on arXiv 8 Oct 2023.

## Builds on (most cited first, 4 of 4)

- [Supervised Typing of Big Graphs using Semantic Embeddings](https://mlsanity.com/paper/1703.07805/) (2017)
- [Information Extraction in Illicit Domains](https://mlsanity.com/paper/1703.03097/) (2017)
- [An Ensemble Blocking Scheme for Entity Resolution of Large and Sparse Datasets](https://mlsanity.com/paper/1609.06265/) (2016)
- [A Data-Driven Study of Commonsense Knowledge using the ConceptNet Knowledge Base](https://mlsanity.com/paper/2011.14084/) (2020)

## Abstract (arXiv)

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.