AI driven web crawling for semantic extraction of news content from newspapers

Area of Science:

• Computer Science
• Artificial Intelligence
• Data Science

Background:

• Traditional web crawlers struggle with diverse data formats in newspaper archives.
• Rule-based and keyword-driven methods lack adaptability and semantic understanding.
• Efficient data extraction is crucial for media, research, and journalism.

Purpose of the Study:

• To develop an intelligent framework for efficient and accurate data extraction from dynamic newspaper databases.
• To overcome the limitations of conventional web crawlers in handling heterogeneous web content.
• To improve data retrieval by integrating Natural Language Processing and deep learning.

Main Methods:

• Proposed WISE (Web-Intelligent Semantic Extractor), a deep learning-based framework.
• Integrated Natural Language Processing (NLP) and neural networks for semantic understanding.
• Developed dynamic crawling strategies based on content semantics and learning patterns.

Main Results:

• WISE achieved 35% higher extraction accuracy and 40% greater processing efficiency compared to conventional crawlers.
• Demonstrated exceptional scalability, contextual accuracy, semantic understanding, and real-time flexibility.
• Showcased superior performance in extracting structured data from heterogeneous sources.

Conclusions:

• WISE offers a novel, intelligent solution for modern web data extraction challenges.
• The framework significantly enhances accuracy and efficiency in retrieving information from newspaper databases.
• WISE provides a scalable, secure, and adaptable approach to web intelligence.