Entity Extraction from Google Drive to Google Sheets

The ability to extract specific pieces of information from documents is becoming more important for many different uses, such as data analysis and information retrieval. This blog will walk through how to automate the process of extracting entities from documents stored in Google Drive and saving them into Google Sheets using 0CodeKit's Entity Detector.

Importance of Data Extraction

Data extraction is the process of identifying and pulling out relevant information from a source for further processing or analysis. This has become increasingly relevant in several fields. For example, in data analysis, the extracted data provides beneficial insights that help in making informed decisions. When it comes to information retrieval, being able to quickly find relevant information saves time and increases productivity. In business intelligence, aggregated data assists in strategic planning and competitive analysis.

How the Automation Works

The first step is to have a Google Drive folder filled with several documents. These documents could be public writings of companies, distributions, or any other relevant content. These documents will be the sources from which data will be extracted.

Secondly, start by downloading a document from the Google Drive folder, which is designed to identify and extract entities from text. After downloading, the document is sent to 0CodeKit’s Entity Detector module. This module processes the document and returns a list of entities, which includes various bundles and data packages.

Once the data is received from 0CodeKit, a text aggregator module can be used to group the entities into a consistent list. The aggregator can be customized to include separators, ensuring that the attributes of the entities are separated by specific characters, making the data more readable and organized. It is also possible to filter out types of entities that are not needed, allowing focus on the most relevant data.

The final step is to transfer the aggregated list into Google Sheets. The goal is to convert the entities into a structured list format and populate a Google Sheets document with this data. Each row in the sheet can represent an entity, with columns for its attributes.

Benefits of the Automation

• Timesaving: It saves a significant amount of time compared to manual processes, allowing focus on more strategic tasks.

• Accuracy and Consistency: Automated processes also reduce the risk of human error, ensuring that the data extraction is consistent and accurate.  

• Scalable: This automation can handle large volumes of documents, making it a scalable solution for extensive data analysis needs.

Wind-Up

This automation not only makes the process more efficient but also ensures accuracy and scalability. It is a practical approach for anyone looking to manage large amounts of data effectively.