Transport & Logistics — Named Entity Extraction for Logistics Data Enrichment

Overview

The purpose of this DAG is to enhance logistics data by extracting named entities from various documents and reports using advanced natural language processing (NLP) techniques. The primary data sources for this DAG include shipping manifests, delivery reports, and inventory logs, which are ingested into the system for processing. The ingestion pipeline first collects these documents and converts them into a structured format suitable for NLP analysis. The processing steps involve several key

The purpose of this DAG is to enhance logistics data by extracting named entities from various documents and reports using advanced natural language processing (NLP) techniques. The primary data sources for this DAG include shipping manifests, delivery reports, and inventory logs, which are ingested into the system for processing. The ingestion pipeline first collects these documents and converts them into a structured format suitable for NLP analysis. The processing steps involve several key stages: first, the documents are tokenized and pre-processed to remove noise and irrelevant information. Next, named entity recognition (NER) algorithms are applied to identify and classify entities such as locations, product names, and service providers. The extracted entities are then validated through quality control checks to ensure accuracy and relevance. In the case of any extraction failures, the DAG logs errors for future review, ensuring continuous improvement of the extraction process. The outputs of this DAG include a structured knowledge graph containing the enriched data, detailed error logs for review, and summary reports highlighting the extracted entities. Monitoring key performance indicators (KPIs) such as extraction accuracy, processing time, and the volume of documents processed is crucial for assessing the effectiveness of the DAG. The business value of this DAG lies in its ability to provide enriched data that supports better decision-making, enhances operational efficiency, and improves overall logistics management.

Part of the Scientific ML & Discovery solution for the Transport & Logistics industry.

Use cases

• Improves data accuracy for logistics decision-making
• Enhances operational efficiency through automated processes
• Reduces manual data entry errors and associated costs
• Enables better compliance and reporting capabilities
• Supports strategic planning with enriched data insights