8 Key Factors for Web Data Curation Using Artificial Intelligence
Are you spending much human effort analyzing data on various websites manually? Are your knowledge workers getting frustrated? Are you interested in automating the tedious stuff? Do you want to do more with less?
Welcome to the world of Artificial Intelligence for web data curation!
“Web data curation is a process of extracting web data, storing and preparing data for further analysis.”
Artificial Intelligence has helped increase operational efficiency and analyze more content with less effort. On the other hand, though there is much potential for applying Artificial Intelligence in various business functions, business leaders often need more awareness about AI’s capabilities. Hence, Convincing businesses to apply Artificial Intelligence becomes challenging.
Ellicium has developed a comprehensive methodology to realize AI’s potential to make value addition to businesses. Taking AI for web data curation as subject matter, this article elaborates on it.
After having helped multiple businesses ranging from start-ups to multi-billion-dollar organizations, we have identified a few critical factors for web data curation using Artificial Intelligence:
Identification of reliable and relevant web sources
Many web sources for every domain claim to hold relevant and up-to-date data. The quality of insights depends on the quality of data. The following points are vital to determining quality data sources:
- Consulting domain experts
- Defining rankings and ratings of websites using an AI program based on the following parameters: Number of hits, Frequency of updating of content on a website, and validation of content by comparing it with different sources
- Using websites with public content
Define Appropriate Web Monitoring Frequency
The website’s content changes periodically based on specific events, but the time between consecutive changes is generally not constant. We set up monitoring frequency depending on the type of website and requirements.
Consider Variations in Website Layouts
The different web data sources have different formats requiring a specific curation process. Generalizing the overall curation process is a challenge here because:
- Every web source can have different metadata or structure
- The content on some web sources is dynamic, where content gets loaded on certain manual events like scrolling, clicking, or hovering.
- With the emergence of new GUI technologies to improve user experience, many web sources change the structure of pages, making maintenance of the AI engine necessary.
Define limits for Web Crawler
A drill-down approach is used while crawling the data from different web sources. But, defining the depth for crawling is a challenge.
Understand Data Security and Accessibility Policies
Many websites have security policies for robotic data extraction, and the challenge is to tune the process based on these policies to avoid conflict with the policies.
Data Format for Different Web Data Sources is Difficult to Generalize
Every web source has a different data format with a different schema. Defining a standard meta-data for each of them is a challenge. And in comes the NoSQL database, which helps you store data with different schemas.
Wisely Chose Algorithm to Determine Relevant Data
To know how we choose and tune different algorithms, refer to our article here.
Design for Scalability
To handle ever-growing web data, we design a scalable system. We have successfully implemented various machine learning algorithms leveraging native parallelism of commodity hardware to speed up the AI process.
As can be observed in many of today’s businesses, like banks using social media analysis for credit rating or LPOs using web crawlers to keep themselves updated on legal happenings, many actionable insights can be drawn from a large amount of well-curated web data. We hope that this article helps you take steps towards growing your business by getting these insights while saving resources.