This paper presents CleanGraph, an interactive web-based tool designed to facilitate the refinement and completion of knowledge graphs. Maintaining the reliability of knowledge graphs, which are grounded in high-quality and error-free facts, is crucial for real-world applications such as question-answering and information retrieval systems. These graphs are often automatically assembled from textual sources by extracting semantic triples via information extraction. However, assuring the quality of these extracted triples, especially when dealing with large or low-quality datasets, can pose a significant challenge and adversely affect the performance of downstream applications. CleanGraph allows users to perform Create, Read, Update, and Delete (CRUD) operations on their graphs, as well as apply models in the form of plugins for graph refinement and completion tasks. These functionalities enable users to enhance the integrity and reliability of their graph data. A demonstration of CleanGraph and its source code can be accessed at https://github.com/nlp-tlp/CleanGraph under the MIT License.

翻译：本文介绍CleanGraph，一个基于交互式网页的工具，旨在促进知识图谱的精炼与补全。维护基于高质量、无错误事实的知识图谱可靠性，对于问答系统、信息检索系统等实际应用至关重要。这些图谱通常通过信息抽取技术从文本源中自动提取语义三元组构建而成。然而，确保所提取三元组的质量，尤其是在处理大规模或低质量数据集时，会构成重大挑战，并对下游应用的性能产生不利影响。CleanGraph允许用户对其图谱执行创建、读取、更新和删除（CRUD）操作，并以插件形式应用模型完成图谱精炼与补全任务。这些功能使用户能够增强图数据的完整性与可靠性。CleanGraph的演示及其源代码可通过MIT许可协议在https://github.com/nlp-tlp/CleanGraph获取。