Neural embedding-based machine learning models have shown promise for predicting novel links in knowledge graphs. Unfortunately, their practical utility is diminished by their lack of interpretability. Recently, the fully interpretable, rule-based al
gorithm AnyBURL yielded highly competitive results on many general-purpose link prediction benchmarks. However, current approaches for aggregating predictions made by multiple rules are affected by redundancies. We improve upon AnyBURL by introducing the SAFRAN rule application framework, which uses a novel aggregation approach called Non-redundant Noisy-OR that detects and clusters redundant rules prior to aggregation. SAFRAN yields new state-of-the-art results for fully interpretable link prediction on the established general-purpose benchmarks FB15K-237, WN18RR and YAGO3-10. Furthermore, it exceeds the results of multiple established embedding-based algorithms on FB15K-237 and WN18RR and narrows the gap between rule-based and embedding-based algorithms on YAGO3-10.
The paper includes two contributions. First, it proves that the series and shunt radiation components, corresponding to longitudinal and transversal electric fields, respectively, are always in phase quadrature in axially asymmetric periodic leaky-wa
ve antennas (LWAs), so that these antennas are inherently elliptically polarized. This fact is theoretically proven and experimentally illustrated by two case-study examples, a composite right/left-handed (CRLH) LWA and a series-fed patch (SFP) LWA. Second, it shows (for the case of the SFP LWA) that the axial ratio is controlled and minimized by the degree of axial asymmetry.
