Attribute-based person search aims to find the target person from the gallery images based on the given query text. It often plays an important role in surveillance systems when visual information is not reliable, such as identifying a criminal from a few witnesses. Although recent works have made great progress, most of them neglect the attribute labeling problems that exist in the current datasets. Moreover, these problems also increase the risk of non-alignment between attribute texts and visual images, leading to large semantic gaps. To address these issues, in this paper, we propose Weak Semantic Embeddings (WSEs), which can modify the data distribution of the original attribute texts and thus improve the representability of attribute features. We also introduce feature graphs to learn more collaborative and calibrated information. Furthermore, the relationship modeled by our feature graphs between all semantic embeddings can reduce the semantic gap in text-to-image retrieval. Extensive evaluations on three challenging benchmarks - PETA, Market-1501 Attribute, and PA100K, demonstrate the effectiveness of the proposed WSEs, and our method outperforms existing state-of-the-art methods.