EAGER: Medical Knowledge Graph Construction from Heterogeneous Sources

本项目的目标是构建全面的医学知识图谱。这种知识图谱可以满足用户对可靠医疗信息日益增长的需求，帮助患者和医生之间进行有效沟通，并可能有助于降低医疗保健的高昂成本。该项目解决了医学领域中观察到的一系列独特挑战，并开发了有效的方法，可以从大量的众包数据中提取知识，以增强医学知识图谱。 本研究通过开发从医疗领域的非结构化数据中挖掘知识的新方法，推进知识图构建和信息可信度分析领域。本课题从以下几个方面研究医疗图构建问题：第一章本文提出了一种有效的方法，在从噪声答案中提取可靠的医学事实时，考虑到医学术语之间的语义关系，2）设计了一个统一的框架，用于在医学知识发现过程中集成来自异构数据源的信息; 3）通过对图中医学术语之间的关系进行推理，完成知识图的构建。所提出的研究被实现为一个系统原型，显示提取的医疗事实和知识图，这有利于在线用户寻求医疗信息。所提出的技术用于提高教育方法。该项目的研究成果被纳入课程材料和项目，加强学生的培训。

The objective of this project is to construct comprehensive medical knowledge graphs. Such knowledge graphs can satisfy users' growing needs for reliable medical information, help the effective communication between patients and doctors, and potentially help reduce the high costs of health care. This project tackles a series of unique challenges observed in medical domains, and develops effective approaches that can extract knowledge from the deluge of crowdsourced data to augment medical knowledge graphs. The proposed research advances the fields of knowledge graph construction and information trustworthiness analysis by developing novel methods that mines knowledge from unstructured data in medical domains.This project investigates the problem of medical graph construction from the following perspectives: 1) Effective approaches are developed to take into account the semantic relations between medical terms during the extraction of reliable medical facts from noisy answers on healthcare question and answering websites; 2) A unified framework is designed to integrate information from heterogeneous data sources in the process of medical knowledge discovery; 3) The knowledge graph is completed by inferring new relations based on existing relations between medical terms in the graph. The proposed research is implemented into a system prototype that displays the extracted medical facts and the knowledge graph, which benefit online users who seek medical information. The proposed techniques are used to enhance educational methodologies. Research results of this project are integrated into course materials and projects that reinforce student training.