A hybrid extraction model for semantic knowledge discovery of water conservancy big data.

ABSTRACT: To address the growing demand for efficient public opinion analysis in water conservancy and related domains, as well as the inefficiencies and limited scalability of existing automated web data extraction algorithms for multi-source datasets, this research integrates advanced technologies including big data analytics, natural language processing, and deep learning. A novel, transferable web information extraction model based on deep learning (WIEM-DL) is proposed, leveraging knowledge graphs, machine learning, and ontology-based methods. This model is designed to adapt to varying website structures, enabling effective cross-website information extraction. By refining water conservancy-related online public opinion content and extracting key feature information from critical sentences, the

SUBMITTER: Feng Y 

PROVIDER: S-EPMC12453640 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature