Software Architecture for Improving Scraping Systems Using Artificial Intelligence

This paper explores diverse innovative Web scraping techniques.It initially introduces a machine learning (ML) approach capable of adapting to changing HTML structures for continuous scraping.Following this, an automated method is detailed, specifically designed to extract data from dense web pages using subject detection and node density techniques.Lastly, the article covers a computer vision-based scraping strategy that employs object recognition and OCR to visually analyse and interpret websites.These methods seek to increase online scraping efficiency and reduce the reliance on HTML structure.The present study proposes an autonomous Web scraper system that integrates Web scraping, Artificial Intelligence (AI), and Natural Language Processing (NLP) techniques, leveraging ChatGPT's natural language understanding capabilities to achieve the desired results.The recommended JavaScript program uses NLP techniques to produce webpage lists for examination and lets users query data in natural language.By utilising cutting-edge AI and ML approaches, it promises to increase Web scraping's usability and effectiveness.The article details upcoming work, such as HTML clean-up and DOM parsing advancements, and examines the benefits of the suggested approach over the currently available tools.