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This work presents a unified knowledge protocol, called UKnow, whichfacilitates knowledge-based studies from the perspective of data. Particularlyfocusing on visual and linguistic modalities, we categorize data knowledge intofive unit types, namely, in-image, in-text, cross-image, cross-text, andimage-text, and set up an efficient pipeline to help construct the multimodalknowledge graph from any data collection. Thanks to the logical informationnaturally contained in knowledge graph, organizing datasets under UKnow formatopens up more possibilities of data usage compared to the commonly usedimage-text pairs. Following UKnow protocol, we collect, from publicinternational news, a large-scale multimodal knowledge graph dataset thatconsists of 1,388,568 nodes (with 571,791 vision-related ones) and 3,673,817triplets. The dataset is also annotated with rich event tags, including 11coarse labels and 9,185 fine labels. Experiments on four benchmarks demonstratethe potential of UKnow in supporting common-sense reasoning and boostingvision-language pre-training with a single dataset, benefiting from its unifiedform of knowledge organization. Code, dataset, and models will be made publiclyavailable.

UKnow: A Unified Knowledge Protocol for Common-Sense Reasoning and Vision-Language Pre-training