State-of-the-art knowledge base completion models, ranging from geometric embeddings to GNNs, achieve impressive predictive performance but share a critical limitation: they act as black boxes that often ignore data consistency, producing predictions that violate rigorous ontological constraints. While neuro-symbolic...
Yi-Zheng Zhao· Proceedings of the ACM on Ma...· 0 citations
This work gives matching algorithms (ConstrainedERM-DP for DP, FairStratified for EO and EqOdds) that are tight up to logarithmic factors and gives matching algorithms that are tight up to logarithmic factors for fairness verification.
Chang Lu, Yi-Zheng Zhao· Proceedings of the 32nd ACM...· 0 citations
The framework is operator-agnostic - it decouples logical structure from the choice of underlying continuous semantics, revealing, through the first controlled comparison of its kind, that this choice alone can swing performance by over 30 points on the same task.
Zi-Han Shao, Chang Lu, Renate A. Schmidt et al.· Proceedings of the 32nd ACM...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.