Transport & Logistics — Named Entity Extraction for Logistics Document Classification
PopularThis DAG facilitates the extraction of named entities from logistics documents, enhancing search and classification capabilities. By normalizing and storing extracted data, it supports advanced recommendation systems and improves operational efficiency.
Overview
The primary purpose of this DAG is to extract named entities from various logistics documents, thereby improving the efficiency of search and classification processes within the transport and logistics sector. The data sources include Enterprise Resource Planning (ERP) systems, Customer Relationship Management (CRM) systems, and other relevant document repositories. The ingestion pipeline begins with the collection of documents from these sources, followed by a parsing step where the content is
The primary purpose of this DAG is to extract named entities from various logistics documents, thereby improving the efficiency of search and classification processes within the transport and logistics sector. The data sources include Enterprise Resource Planning (ERP) systems, Customer Relationship Management (CRM) systems, and other relevant document repositories. The ingestion pipeline begins with the collection of documents from these sources, followed by a parsing step where the content is analyzed for named entities. Once identified, the data undergoes normalization to ensure consistency in format and representation. Quality control measures are implemented throughout the process to validate the accuracy of the extracted entities, including checks for duplicates and relevance. The final output is stored in a centralized data warehouse, making it readily accessible for further analysis. Additionally, the results are exposed through a robust API, allowing seamless integration with search and recommendation systems. Key performance indicators (KPIs) such as extraction accuracy, processing time, and data completeness are monitored to assess the effectiveness of the DAG. The business value lies in enhanced document retrieval, improved classification accuracy, and ultimately, better decision-making capabilities for logistics operations.
Part of the Supply/Demand Forecast solution for the Transport & Logistics industry.
Use cases
- Improves search efficiency for logistics documents
- Enhances classification accuracy for better data management
- Facilitates informed decision-making in logistics operations
- Reduces time spent on manual data entry and processing
- Supports advanced analytics and recommendation systems
Technical Specifications
Inputs
- • ERP transaction logs
- • CRM customer interaction records
- • Logistics document repositories
Outputs
- • Normalized named entity dataset
- • Quality control reports
- • API endpoints for data access
Processing Steps
- 1. Collect documents from ERP and CRM systems
- 2. Parse documents to identify named entities
- 3. Normalize extracted entities for consistency
- 4. Apply quality control checks on the data
- 5. Store results in a centralized data warehouse
- 6. Expose data through an API for external access
Additional Information
DAG ID
WK-1238
Last Updated
2025-10-24
Downloads
105