Energy — Entity Extraction and Taxonomy Creation for Enhanced Document Search

Overview

The primary purpose of this DAG is to leverage Named Entity Recognition (NER) techniques to identify and categorize relevant data within energy-related documents. The architecture consists of a robust data ingestion pipeline that regularly updates its inputs to reflect the latest developments in the energy sector. The process begins with the ingestion of various data sources, including regulatory documents, technical reports, and industry publications. Once ingested, the NER processing step iden

The primary purpose of this DAG is to leverage Named Entity Recognition (NER) techniques to identify and categorize relevant data within energy-related documents. The architecture consists of a robust data ingestion pipeline that regularly updates its inputs to reflect the latest developments in the energy sector. The process begins with the ingestion of various data sources, including regulatory documents, technical reports, and industry publications. Once ingested, the NER processing step identifies key entities such as organizations, locations, and technical terms, which are then classified into a structured taxonomy. This taxonomy not only enhances the searchability of documents but also provides a framework for knowledge management within the organization. Quality controls are implemented to ensure high accuracy in entity extraction, with KPIs focused on extraction precision rates and update frequencies. The final outputs include a detailed taxonomy and a searchable database of extracted entities, which facilitate better data accessibility and decision-making. Monitoring tools track performance metrics, ensuring continuous improvement in the extraction process. The business value lies in improved operational efficiency, enhanced compliance tracking, and accelerated decision-making processes within the energy industry.

Part of the Document Automation solution for the Energy industry.

Use cases

• Improved document search efficiency and accuracy
• Enhanced compliance management through organized data
• Faster decision-making with readily accessible information
• Increased operational efficiency in knowledge handling
• Better alignment with industry standards and regulations