A Semi-automatic Data Extraction System for Heterogeneous Data Sources: A Case Study from Cotton Industry

TL;DR

This paper introduces a semi-automatic system that extracts focused information from diverse unstructured data sources, like PDFs and HTML, using text mining, demonstrated through a case study in the cotton industry.

Contribution

The paper presents a novel data extraction system capable of handling heterogeneous data formats with a case study application in the cotton industry.

Findings

System effectively extracts relevant information from multiple data formats.

Qualitative analysis shows high adaptability and usefulness in the cotton industry.

Demonstrates the potential for semi-automatic data extraction in industry-specific contexts.

Abstract

With the recent developments in digitisation, there are increasing number of documents available online. There are several information extraction tools that are available to extract information from digitised documents. However, identifying precise answers to a given query is often a challenging task especially if the data source where the relevant information resides is unknown. This situation becomes more complex when the data source is available in multiple formats such as PDF, table and html. In this paper, we propose a novel data extraction system to discover relevant and focused information from diverse unstructured data sources based on text mining approaches. We perform a qualitative analysis to evaluate the proposed system and its suitability and adaptability using cotton industry.