Table is an efficient way to represent a huge number of facts in a compact manner. As practitioners in the vertical domain share lots of common prior knowledge, they tend to represent facts more concisely using matrix-style tables. However, such tables are originally intended for human reading, but not machine-readable due to their complex structures including row header, column header, metadata, external context, and even hierarchies in headers. In order to improve the efficiency of practitioners in mining and utilizing these matrix-style tables, in this study we introduce a challenging task to discover fact-overlapping relations between matrix-style tables. This relation focuses on fine-grained local semantics instead of overall relatedness in conventional tasks. We propose an attention-based model for this task. Experiments reveal that our model is more capable of discovering the local relatedness, and outperforms four baseline methods. We also conduct an ablation study and case study to investigate our model in detail.