  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.
