标题： 通过自动化集成数据生成可靠的不良事件概况（GRAPH-AID）：一种半自动本体构建方法 显示英文标题

标题： Generating Reliable Adverse event Profiles for Health through Automated Integrated Data (GRAPH-AID): A Semi-Automated Ontology Building Approach

摘要： 随着数据和知识的迅速扩展，采用系统的方法论来进行本体生成变得至关重要。 随着数据量的每日增加和内容的频繁变化，对数据库存储和检索信息以创建知识图谱的需求变得更加紧迫。 之前建立的知识获取与表示方法（KNARM）概述了一种系统的方法来应对这些挑战并创建知识图谱。 然而，遵循此方法突显了现有挑战，即无缝集成Neo4j数据库与网络本体语言（OWL）。 之前讨论过将Neo4j中的数据集成到本体中的尝试，但这些方法通常需要了解描述逻辑（DL）语法，这可能对许多用户来说并不熟悉。 因此，需要一种更易于使用的方法来弥合这一差距。 本文提出了一种用户友好的方法，利用Python及其rdflib库来支持本体开发。 我们通过一个由整合食品和药物管理局（FDA）不良事件报告系统（FAERS）数据库数据而创建的Neo4j数据库展示了我们的新方法。 使用这个数据集，我们开发了一个Python脚本，可以自动生成所需的类及其公理，从而促进更顺畅的集成过程。 这种方法为在快速增长的不良药物事件数据集背景下本体生成的挑战提供了一个实际的解决方案，支持改进的药物安全监测和公共卫生决策。 显示英文摘要

摘要： As data and knowledge expand rapidly, adopting systematic methodologies for ontology generation has become crucial. With the daily increases in data volumes and frequent content changes, the demand for databases to store and retrieve information for the creation of knowledge graphs has become increasingly urgent. The previously established Knowledge Acquisition and Representation Methodology (KNARM) outlines a systematic approach to address these challenges and create knowledge graphs. However, following this methodology highlights the existing challenge of seamlessly integrating Neo4j databases with the Web Ontology Language (OWL). Previous attempts to integrate data from Neo4j into an ontology have been discussed, but these approaches often require an understanding of description logics (DL) syntax, which may not be familiar to many users. Thus, a more accessible method is necessary to bridge this gap. This paper presents a user-friendly approach that utilizes Python and its rdflib library to support ontology development. We showcase our novel approach through a Neo4j database we created by integrating data from the Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) database. Using this dataset, we developed a Python script that automatically generates the required classes and their axioms, facilitating a smoother integration process. This approach offers a practical solution to the challenges of ontology generation in the context of rapidly growing adverse drug event datasets, supporting improved drug safety monitoring and public health decision-making.