Web Scrapings

How Can AI Be Used in Web Scraping?

In the online world of the internet, data is everything, especially if you’re running an e-commerce business. You need new data daily to improve your decision-making process and find out how to appeal to your consumers. Web scrapings help you gather data by automating the process and refining the data pipeline.

However, there are a few limitations in scraping and extracting data safely from the best websites. Since web scrapings requires a bit of knowledge and constant monitoring, it all comes down to choosing the right tools for the job, and the best tool for it is artificial intelligence. Let’s see how it can be used in web scrapings.

Definition of web scrapings

Web scraping exists almost as long as the web itself. It’s the lifeblood of search engines like Google and helps internet users extract valuable data in heeps. Its goal is to automatically gather data from multiple, various, and high-quality websites. Web scraping involves using a scraping bot to go over hundreds of web pages to collect data.

However, top websites don’t take kindly to scraping bots, and they have various security mechanisms set in place to prevent the bots from reaching the data. Simple bots are easy to detect, but more sophisticated bots have a way around the security mechanisms.

They are powered by AI to identify high-quality data on a website and extract it for analysis without getting detected, blocked, or banned.

Obstacles in web scrapings

Even though web scraping is extremely useful, it comes with many obstacles. Recently, the U.S. Supreme Court has made a rule that web scraping for AI and analytics can be legal. However, even with this being so, there are still many obstacles in web scraping that might prevent you from gathering valuable data that can make your business more successful.

Some of the most common obstacles include:

  • Scalability of web scraping – scraping a single web page isn’t a problem per se, but scraping at a huge scale, as in scraping millions of websites simultaneously, poses a few challenges here and there. Aside from the risk of getting detected and banned, the challenges include maintaining a data warehouse, collecting data, and managing the codebase.
  • Pattern changes – websites tend to periodically change their user interface, making it harder for scrapers to do their job.
  • Anti-scraping technologies – top-quality websites use anti-scraping mechanisms and security technologies.
  • Dynamic content based on JavaScript – modern websites make data extraction additionally challenging by using Ajax and JavaScript to render dynamic content.
  • Honeypot traps – certain top-class websites use honeypot traps within web pages to detect scraping bots and feed them false data.
  • Data quality – there are certain quality guidelines that apply to extracted data. If the quality is poor, it can affect the integrity of extracted information.

AI is changing things

AI web scraping helps solve many problems and overcome challenges that come with data scraping and extraction. Many companies use AI web scraping tools to gather quality information, including market research, enterprise data capture, supply chain analytics, labor research, e-commerce, and so on.

AI helps make web scraping more effective by making the scraping bots more intuitive. Thanks to AI technologies like NLP and machine learning, scraping bots can learn which sections of a website contain valuable data such as product prices, reviews, and descriptions. By combining AI with web scraping, you can make the entire process of data augmentation more effective and efficient.

AI web scraping allows for more effective data extraction, cleansing, aggregation, and normalization. It helps become more effective while saving both resources and time. Instead of wasting huge amounts of time on data gathering, you can shift your focus on the core mission, knowing that AI has your web scraping needs covered in full.

AI helped create data augmentation techniques, including:

  • Extrapolation
  • Tagging
  • Aggregation
  • Probability techniques

AI web scraping

AI helps improve scraping by making it more resilient. Since websites are built for humans and not machines, extracting across different web pages at a large scale can be a challenging business. There is simply too much room for mistakes.

However, the immense power of AI can help avoid many of the most common mistakes and make scraping much more effective. It can also reduce data misuse and errors, as well as improve data structure, making the extracted data more usable and suitable for different applications. For example, last year, Oxylabs launched an AI & ML-powered solution that handles IP blocks and website changes.

The more AI continues to evolve, the more it helps improve web scraping and makes scraping more efficient and smooth than ever before.

Conclusion

AI is present in every aspect of our lives these days, and every vertical of modern society relies on this amazing technology, one way or the other. Since web scrapings and data extraction are gaining more momentum with each passing day, introducing AI into your web scraping techniques was just a matter of time.

If you want to make sure your scraping efforts result in top-quality, usable data, AI-driven, smart web scraping is the best way to go.

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