Web scraping is used for, among other things, getting the vast volumes of publicly available data needed for training algorithms for machine learning (ML). The relationship between data scraping and ML is, however, symbiotic rather than one-sided. On the other side is ML’s ability to improve the fundamental procedures underlying web data gathering, making it more efficient and capable of producing desired results. This article will concentrate on one such process intrinsic to web scraping – data parsing, and how it can benefit from AI and ML.

The Challenges of a Rules-Based Process

People get frustrated when they are stuck with mundane, repetitive tasks for extended periods of time – for example, copy-pasting multiple data points from many sources. Web scraping is a far better alternative to gathering data manually, enabling large-scale automated data collection. It does, however, have its own set of repeating mundane tasks.

Web scrapers and data parsers are generally obedient digital creatures. Tell them where and what kind of data to scrape, define clear rules for structuring that data, and they will provide you with the appropriate output.

A data parser will get some of the most important job done in web data collection. Following the pre-defined rules, it will remove useless information like tags and blank spaces from the raw HTML data and put the useful data in CSV, JSON, or other readable format. Thus, rules-based data parsing will take the messy scraped data and convert it into structured, readable information.

The trouble with perfectly obedient creatures is that they will only do what the instructions tell them. Unfortunately, you can’t define rules once and for all possible websites and changing conditions in them. 

Many websites are dynamic – they do not have a stable structure that would allow leaving a rules-based parser to do the work unattended. For example, e-commerce websites frequently change their layout, which requires adapting the dedicated parsers accordingly to continue parsing. Building a custom parser to suit each website format is a task that eats up developers’ time and significantly slows down data collection.

Whenever website structure changes happen, rules-based parsing will break down, no longer producing the intended results. Once again, developers will have a frustrating and time-consuming task on their hands that will prevent putting their costly hours to more productive use.

Due to the challenges of rules-based data parsing, businesses are looking for a way to take data gathering automation a big step forward with the help of AI and ML.

What Do We Talk About When We Talk About ML?

Machine learning and other AI-related terms are now buzzwords, thrown around quite offhandedly in the general media. Sometimes the same term is used to refer to different things or two terms with different meanings are used interchangeably.

Therefore, even when talking to an audience that is familiar with the topic, it is worthwhile to explicate how these terms are used to avoid misunderstandings.

We can start with the broad definition of AI as the simulation of human intelligence in machines. Machine learning models are then specific applications of AI capable of simulating not only human-like problem-solving but a particular feature of human intelligence – learning capacity.

In practice, machine learning models are trained by feeding them large amounts of data relevant to carrying out particular tasks. The models then learn patterns and similarities in these types of data, enabling them to predict and recognize certain outcomes. Thus, ML algorithms can “figure out” what to do even when they were not specifically programmed to do it.

The three main machine learning paradigms are the following:

• Supervised learning, using prelabeled input and output datasets to train algorithms to classify data and predict outcomes accurately.
• Unsupervised learning, which allows algorithms to recognize patterns in raw data without human intervention.
• Reinforced learning, where the ML model learns to solve the problem by receiving feedback on its previous decisions. Before receiving any feedback, the model chooses randomly as it has no information.

A specific subfield of ML, deep learning (DP), is also relevant to data parsing. Deep learning refers to algorithm training that utilizes hierarchical layers of neural networks to process and learn from data, mimicking human brain-like architectures.

ML for Data Parsing

The ability of ML algorithms to recognize patterns and make decisions without additional coding allows for solving many of the pressing problems of rules-based processes.

One of the main stages of supervised machine learning consists of teaching the classification model by feeding it pre-labeled data sets. Granted, it requires a lot of data and time to label it; building a parser this way will be a longer process than simply precoding rules and templates for parsing. But it is likely to prove worthwhile by reducing hours spent and the effort required for maintenance.

Trained to classify data properly, an ML model can adapt to various website layouts and coding styles and keep going even when structural differences are encountered. Thus, your developers are no longer held back by constantly having to fix and relaunch parsers.

Unsupervised or semi-supervised deep learning teaches parsers to identify similarities and patterns in the HTML data collected from public websites. Trained this way, parsers are not stuck with one notion of where to find specific data in the website’s structure. Rather it can adapt and seek out the specific type of information.

Therefore, for example, you can train an adaptive parser to scrape and parse various ecommerce sites effectively. Regardless of how the site’s HTML data is structured, the parser will know how to convert it into structured and relevant data. What you receive will be precisely the filtered product descriptions, prices, and other information that you might need.

Adaptive, ML-based parsers are also capable of handling dynamic, JavaScript-heavy websites. Having been trained on various layouts for thematically uniform websites, parsers will find the targeted data even after frequent layout changes. This will prevent errors and improve the robustness of the data collection process.

The Way Forward

It is only a question of time (and probably not that much time) when rules-based data parsing becomes obsolete. The advantages of AI and ML applications for web intelligence are too great to neglect. The main tasks that lie ahead of us are related to finding the most effective ways of unsupervised machine learning for web scraping automation.