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.

翻译：高效寻找医生与地点是医疗领域患者面临的重要搜索问题，传统信息检索方法在此场景下往往难以达到最优效果。近十年来，知识图谱（KGs）作为一种融合半结构化数据语义建模、自然语言处理技术（如信息抽取）与结构化查询语言（如SPARQL和Cypher）鲁棒查询能力的强效工具，已逐渐崛起。本文基于短论文形式，提出了一种面向医疗领域稳健定位医生与地点的知识图谱搜索引擎架构。初步结果表明，该方法可在不降低查询质量的前提下，显著提升复杂查询的覆盖率。