Knowledge graph completion (KGC) aims to predict missing links between entities based on known relational facts. Text-based methods typically leverage pretrained language models to extract semantic representations of entities, and some of these methods further incorporate graph structure information to enhance the representations. Despite these advances, current approaches face two significant limitations: insufficient integration of semantic and structural information, and incomplete neighborhood context representation that neglects tail-entity perspectives. In this work, we introduce a progressive semantic framework enhanced by structural signals, which is trained in stages by first establishing robust semantic representations and then gradually integrating graph attention to incorporate structural context. It also includes a bidirectional neighborhood aggregation mechanism that captures both head- and tail-entity contexts to enrich relational understanding. Experiments on two public datasets demonstrate the effectiveness of our method in improving KGC performance while keeping the architecture simple and lightweight.