In response to the growing demand for knowledge-intensive operations and the widespread adoption of cloud-based document management systems, this study proposes a comprehensive framework for high-quality knowledge acquisition and intelligent value augmentation in office scenarios. Addressing key challenges such as the difficulty in processing unstructured documents, low accuracy in knowledge extraction and limited reusability in smart office environments, we design a multi-stage knowledge processing pipeline. This includes document structure restoration, semantic representation using BERT-based models, named entity recognition with BiLSTM-CRF, relation extraction and entity disambiguation via cosine similarity. The methodology leverages state-of-the-art NLP techniques to ensure accurate entity recognition and relationship extraction, with a focus on overcoming semantic ambiguity and improving task relevance. The extracted knowledge is fused into structured triples and organized into a dynamically evolving knowledge graph guided by ontology constraints and external knowledge integration (e.g., Wikidata). This continuous evolution of the knowledge graph allows for adaptive learning and improves recommendations based on user feedback and real-time office needs. On top of this graph, we implement intelligent services including semantic QA, task recommendation via GAT and document-driven reasoning. Experimental results on over 42,000 real-world enterprise documents across domains (government, finance, manufacturing) demonstrate that our method achieves superior performance in precision (0.93), recall (0.91) and fusion consistency (0.89), significantly outperforming baseline methods.
Zhou et al. (Sun,) studied this question.