  Multi-modal knowledge graphs have emerged as a powerful approach for
information representation, combining data from different modalities such as
text, images, and videos. While several such graphs have been constructed and
have played important roles in applications like visual question answering and
recommendation systems, challenges persist in their development. These include
the scarcity of high-quality Chinese knowledge graphs and limited domain
coverage in existing multi-modal knowledge graphs. This paper introduces
MMPKUBase, a robust and extensive Chinese multi-modal knowledge graph that
covers diverse domains, including birds, mammals, ferns, and more, comprising
over 50,000 entities and over 1 million filtered images. To ensure data
quality, we employ Prototypical Contrastive Learning and the Isolation Forest
algorithm to refine the image data. Additionally, we have developed a
user-friendly platform to facilitate image attribute exploration.
