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

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 cannot reveal 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.

Do Embedding Models Perform Well for Knowledge Base Completion?

In this work, we put into question the effectiveness of the evaluation methods currently used to measure the performance of latent factor models for the task of knowledge base completion. We argue that by focusing on a small subset of possible facts in the knowledge base, current evaluation practices are better suited for question answering tasks, rather than knowledge base completion, where it is also important to avoid the addition of incorrect facts into the knowledge base. We illustrate our point by showing how models with limited expressiveness achieve state-of-the-art performance, even while adding many incorrect (even nonsensical) facts to a knowledge base. Finally, we show that when using a simple evaluation procedure designed to also penalize the addition of incorrect facts, the general and relative performance of all models looks very different than previously seen. This indicates the need for more powerful latent factor models for the task of knowledge base completion.