Structural health monitoring (SHM) research generates vast amount of information, especially as unstructured data formats. To date, most natural language processing (NLP) applications focus on extracting information (syntactic or semantic level) rather than providing latent knowledge and generating newer information (pragmatic level). Thus, this study proposes a pragmatic NLP framework integrating named entity recognition (NER) model (BERT–BiLSTM–CRF), domain-specific knowledge graph (KG), and hypothesis generation. Using a labeled dataset, the semantic-aware NER model achieved 0.8998 accuracy and 0.8705 F1 score, allowing precise label prediction for unseen texts. Then, domain-specific KG constructed interrelations across diverse literature, blending insights. From this enriched KG, the framework generated candidate hypotheses to provide latent knowledge. In this work, the generated hypothesis is validated by showing a strong correlation to the literature. The results of this study showed the potential of pragmatic NLP on SHM, offering pathways for latent knowledge reasoning and cross-disciplinary research insight discovery.