Machine Learning and Web Scraping | ESK Solutions Guide
If you've been keeping up with the news lately, you may have been amazed by the latest machine learning innovations—like intelligent smartphones on the market, as well as self-driving cars that are about to hit the streets in the coming year and make roads accident-free. But let me tell you: even your business can benefit from simple machine intelligence.
Machine Learning and Data Scraping
Many companies have been using scrapers for a long time. In the past, scraping meant having ten interns sit and search for data on your topic, or competitor information, or even product details to fill Excel sheets. Today, you can subscribe to scraping services that use bots to perform these tasks without any manual intervention, speeding up the process and improving reliability and data accuracy.
When it comes to artificial intelligence, there is some difference between machine learning and AI. In machine learning, the user must teach the machine what is right and wrong—i.e., provide it with a set of rules and a set of training examples.
This training process is important for achieving greater accuracy in tasks. The more it is trained, and the quality of the data it is trained on, will determine its performance in subsequent stages. In the case of artificial intelligence, also known as unsupervised learning, training happens on its own, with a loosely connected set of rules and minimal training.
It can create its own path as it goes. As it is used, it learns more and can perform better. This is made possible through artificial neural networks and deep learning, which are commonly used for speech and object recognition, sentiment analysis, image segmentation, natural language processing, as well as recognition and imitation of human movements.
The Internet is the largest data repository—vast and rich. The immense opportunities opened by such an incredible amount of data are unimaginable with pen and paper.
However, the challenge lies in how to use this raw data to obtain meaningful and intelligent information. Collecting data from the Internet takes time and effort, despite the fact that website data parsing technologies have come a long way. But the situation is changing, and labs like MIT are working on creating intelligent systems that can gather information from multiple online sources and even teach themselves how to do it.
Extracting structured data from unstructured documents can be automatically performed using such research methods. Simply put, these studies are about systems that will think like a human when reviewing documents.
When we cannot find some information to fill a missing gap in a document, we try to fill it with alternative information. The algorithm does the same and stores this newly found information in its repository.
AI-Based Data Extraction Systems
AI-based data extraction systems involve a so-called "confidence score". This metric determines the probability that a classification performed by the machine is statistically correct, and is determined based on the data it was trained on up to that point. If the calculated confidence score does not meet the user-defined threshold, the system will automatically search the Internet and retrieve more relevant data.
Once the confidence score is achieved, the new data is integrated with the original document and presented to you. This is a cyclical process where the machine tries to collect the entire data bank you need, picking pieces and fragments here and there, then computing the confidence score and returning if a batch of data doesn't meet the threshold.
This learning mechanism is known as "reinforcement learning" and rewards itself for correct finds. That is, upon finding satisfactory data that exceeds the threshold score, the machine not only provides it to the user but also stores all related information so that the next time it performs a similar task, it already knows which paths to take. The machine tries to combine data from various sources without affecting overall accuracy, keeping the final result as close to the required threshold as possible.
To test how well such an AI system can extract data, MIT researchers gave it a sample test task.
The machine was tasked with analyzing online information about mass shootings in the United States and somehow gathering the shooter's name as well as other details such as the number of injured, deceased, and the location. The system managed to obtain accurate data, outperforming conventional data search mechanisms by more than ten percent.
In Conclusion
Given the ever-growing need for data and its scraping, and the known challenges associated with obtaining it, artificial intelligence or machine learning could be the missing piece in the whole equation.
Research in this area, though in its early stages, is very promising and gives us a glimpse into a future where intelligent bots with human-like capabilities will be able to crawl the Internet and extract the information we need.
This could be a game-changer in research tasks where many people perform manual work to collect important data that cannot be easily found, or for solving data problems in business that traditional scraping tools cannot handle.
New research in this field and companies encouraging more and more scraping and data processing will help service providers invest more in intelligent scrapers, and perhaps these services will soon become the best friends of both researchers and businesses. So, maybe you don't need a Terminator to work for you, just an intelligent crawler script.