The University of Mannheim, founded in 1907, is one of Germany’s leading research universities, with a particular emphasis on the social and economic sciences. Housed mainly in the Mannheim Baroque Palace, it is recognized for its triple-accredited business school and its mission to integrate teaching and research for international competitiveness. NECLA and the University of Mannheim co-authored research on exploiting large-scale vision-and-language models to address low-supervision challenges. We collaborated to gain insights into how AI systems can learn effectively from unlabeled data, thereby optimizing performance in applications that require scalability and minimal manual labeling.

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KGxBoard: Explainable and Interactive Leaderboard for Evaluation of Knowledge Graph Completion Models

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Knowledge Graphs (KGs) store information in the form of (head, predicate, tail)-triples. To augment KGs with new knowledge, researchers proposed models for KG Completion (KGC) tasks such as link prediction, i.e., answering (h, p, ?) or (?, p, t) queries. Such models are usually evaluated with averaged metrics on a held-out test set. While useful for tracking progress, averaged single-score metrics cannotreveal what exactly a model has learned — or failed to learn. To address this issue, we propose KGxBoard: an interactive framework for performing fine-grained evaluation on meaningful subsets of the data, each of which tests individual and interpretable capabilities of a KGC model. In our experiments, we highlight the findings that we discovered with the use of KGxBoard, which would have been impossible to detect with standard averaged single-score metrics.

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