Energy — Taxonomy Data Extraction and Structuring Pipeline
FreeThis DAG extracts and structures taxonomy data from multiple sources to enhance semantic search capabilities. It ensures data quality and integration into a knowledge graph, ultimately improving business intelligence in the energy sector.
Overview
The purpose of this DAG is to extract and structure taxonomy data to facilitate improved semantic search within the energy industry. It ingests data from various sources, including internal documents and business APIs, to identify named entities and their relationships. The data ingestion pipeline begins with the collection of unstructured and structured data, followed by normalization processes that prepare the data for integration. Named entity recognition and relationship extraction are perfo
The purpose of this DAG is to extract and structure taxonomy data to facilitate improved semantic search within the energy industry. It ingests data from various sources, including internal documents and business APIs, to identify named entities and their relationships. The data ingestion pipeline begins with the collection of unstructured and structured data, followed by normalization processes that prepare the data for integration. Named entity recognition and relationship extraction are performed to populate a knowledge graph, which serves as the backbone for the semantic search engine. Quality control measures are implemented throughout the process to ensure the accuracy and reliability of the extracted data. These measures include validation checks and consistency assessments, which are critical for maintaining data integrity. The outputs of this DAG include enriched business dictionaries and enhanced search capabilities that allow users to access relevant information quickly. Monitoring key performance indicators (KPIs) such as data accuracy rates and processing time helps in assessing the effectiveness of the pipeline. The business value lies in the improved ability to retrieve and utilize data, leading to better decision-making and operational efficiencies in the energy sector.
Part of the Literature Review solution for the Energy industry.
Use cases
- Improves data retrieval efficiency for energy professionals
- Enhances decision-making through better data insights
- Reduces time spent on manual data processing tasks
- Increases accuracy of information accessed by users
- Facilitates compliance with industry regulations through structured data
Technical Specifications
Inputs
- • Internal documents containing taxonomy data
- • APIs providing business-related information
- • CSV files with structured entity data
Outputs
- • Knowledge graph populated with extracted entities
- • Enriched business dictionaries for semantic search
- • Reports on data quality and extraction metrics
Processing Steps
- 1. Ingest data from internal documents and APIs
- 2. Normalize and preprocess the ingested data
- 3. Extract named entities and relationships
- 4. Integrate processed data into the knowledge graph
- 5. Conduct quality control checks on extracted data
- 6. Generate enriched business dictionaries
- 7. Monitor and report on processing performance
Additional Information
DAG ID
WK-0897
Last Updated
2025-11-11
Downloads
8