The paper introduces a new method to query knowledge graphs via probabilistic embeddings based on the Beta distribution. The paper is well written and relevant to the NeurIPS community. All reviewers and the AC support acceptance of the paper for its contributions, notably since it proposes a novel and promising approach that enables logical negation and FOL queries on KG embeddings and as such extends the applicability of embeddings for KG inference tasks. However, please consider revising your paper to take feedback from reviewers into account e.g., in particular regarding the concerns raised related to empirical evaluation and theoretical analysis.