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Wrapper Induction Algorithms for Information Extraction

Definition and Benefits
Wrapper Induction Algorythms

In the field of information extraction, there is a research niche known as wrapper induction algorithms. We can use these algorithms to extract structured data automatically from unstructured sources. So, let’s discover what wrapper induction is and the most popular induction wrapper algorithms.

What are Wrapper Induction Algorithms?

Wrapper induction algorithms can automatically extract structured data from unstructured sources. This process is known as data wrapping. The main goal of wrapper induction algorithms is to minimize the manual input required to extract the desired data.

There are two main types of wrapper induction algorithms.

  1. Supervised algorithms: require a data set with pre-labelled data to learn how to extract the desired information.
  2. Unsupervised algorithms: learn how to extract the desired information by analyzing the structure of the unstructured source itself.

Some of the most popular wrapper induction algorithms include AutoRM, SYNTHIA, Dual-TLBO, EXALG, RoadRunner, FivaTech, TEX, and DCADE.

Why Use Wrapper Induction Algorithms?

Wrapper Induction for Automatic Information Extraction

1. Avoid Manual Input

Wrapper induction algorithms are particularly helpful when you need to automatically extract structured data from unstructured sources. For example, when working with a large dataset that contains a lot of unstructured data, a wrapper induction algorithm allows you to automatically extract the desired information without the need for manual input.

2. Save Time and Effort

Another benefit of induction wrapper algorithms is that they can save you time and effort. Try to extract the desired information manually: it would likely take you a significant amount of time – time that you could better spend on other tasks.

3. Fix Issues Fast

Additionally, if you were to use a wrapper induction algorithm and something went wrong during the extraction process, you would be able to quickly fix the issue and re-run the algorithm without having to start from scratch.


Wrapper Induction algorithms are a helpful tool for automatically extracting structured data from unstructured sources. When you need to collect structured data from an unstructured source, a wrapper induction algorithm can save you time and effort while ensuring that the extracted data is accurate.

Our goal is to integrate the state-of-the-art unsupervised wrapper induction algorithm into our Spark-based acquisition engine.

Credits: featured Image by upklyak on Freepik

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