Cross-Domain Ontology Synthesis for Semantic Web Integration: A Neural Network Approach

Semantic Web Integration is a significant challenge in the domain of data mining and web technology due to the diverse and dynamic nature of web resources. The objective of this research is to propose a novel approach for Cross-Domain Ontology Synthesis employing neural network models to enhance semantic web integration. This paper introduces a scalable and efficient framework that combines the robustness of deep learning techniques with the semantic richness of ontologies, facilitating a seamless integration of heterogeneous data sources. The methodology encompasses a systematic extraction of domain-specific features and relationships through convolutional neural networks and recurrent neural networks, ensuring the adaptability and accuracy of ontology synthesis. The proposed model is validated against various benchmarks and datasets, demonstrating its superiority in terms of precision, recall, and semantic coherence compared to traditional methods. This research contributes to the field by addressing the semantic gap and promoting interoperability among disparate web entities, leading to a more coherent and interconnected semantic web. The findings indicate that leveraging neural network architectures in ontology synthesis significantly improves the integration process, paving the way for advanced applications in web technology, data mining, and beyond.