MMPKUBase: A Comprehensive and High-quality Chinese Multi-modal Knowledge Graph
This provides a valuable resource for researchers and developers working on Chinese multi-modal AI applications, though it is incremental as it builds on existing knowledge graph concepts.
The paper tackles the scarcity of high-quality Chinese multi-modal knowledge graphs by introducing MMPKUBase, a comprehensive dataset with over 50,000 entities and 1 million filtered images across diverse domains.
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.