On the Semantic Web, resources are connected to each other by the IRI. As the basic unit is comprised of linked data, machines can use semantic data and reason their relations without additional intervention on the Semantic Web. However, it is necessary for users who first encounter the Semantic Web to understand its underlying structure and some grammatical rules. This study suggests linking data sets of the Semantic Web through the Euler diagram, which does not require any prior knowledge. We performed a user study with our relationship-building system and verified that users could better understand linked data through the usage of the system. Users can indirectly be guided by using our Euler diagram-based data relationship-building system to understand the Semantic Web and its data linkage system. We also expect that the data sets defined through our system can be used in various applications.

In this report, we introduce NICE (New frontiers for zero-shot Image Captioning Evaluation) project and share the results and outcomes of 2023 challenge. This project is designed to challenge the computer vision community to develop robust image captioning models that advance the state-of-the-art both in terms of accuracy and fairness. Through the challenge, the image captioning models were tested using a new evaluation dataset that includes a large variety of visual concepts from many domains. There was no specific training data provided for the challenge, and therefore the challenge entries were required to adapt to new types of image descriptions that had not been seen during training. This report includes information on the newly proposed NICE dataset, evaluation methods, challenge results, and technical details of top-ranking entries. We expect that the outcomes of the challenge will contribute to the improvement of AI models on various vision-language tasks.