A Hybrid Approach for Query-Based Data Extraction Using Ensemble BERT Model with Walrus Optimization Algorithm

The growing volume of digital text complicates the extraction of relevant information from unstructured data. Transformer models such as BERT, ALBERT, and RoBERTa are powerful, but they may face challenges in hyperparameter optimization and adaptation to new domains. To address this issue, a hybrid ensemble BERT model is suggested, optimized using the Walrus Optimization Algorithm (WaOA). The framework applies PCA to reduce dimensionality, ontology normalization, and K-means clustering to improve semantic comprehension. Experimental results on the SQuAD 2.0 and MS MARCO datasets show that the proposed model outperforms the baseline models. WaOA (Weighted Average of Attention) can improve convergence, reduce training time, and enhance prediction accuracy. The model also improves the semantic relevance of the extracted information. Attention maps visualize the model’s focus on relevant query terms. The method enhances efficiency and cuts redundancy. It also provides a more generalized approach to different query types. The framework promotes consistent and reliable performance across different data conditions, including varying input formats and varying noise levels. It can be generalized to multilingual and domain-specific applications. Overall, the framework provides a scalable and reliable solution to real-world information extraction.