Retail — E-Commerce Taxonomy Generation and Entity Identification Pipeline

Overview

The primary purpose of this DAG is to create structured taxonomies and identify critical entities from retail data, which enhances the overall data organization and retrieval processes. The workflow begins with the ingestion of normalized data sources, such as product catalogs, customer feedback, and transaction records. The data is processed through a series of steps that include Named Entity Recognition (NER) to extract relevant entities, followed by the categorization of these entities into p

The primary purpose of this DAG is to create structured taxonomies and identify critical entities from retail data, which enhances the overall data organization and retrieval processes. The workflow begins with the ingestion of normalized data sources, such as product catalogs, customer feedback, and transaction records. The data is processed through a series of steps that include Named Entity Recognition (NER) to extract relevant entities, followed by the categorization of these entities into predefined taxonomies. Quality control measures are implemented at each stage to ensure the accuracy and reliability of the extracted entities and their classifications. The processed data is then integrated into a knowledge graph, facilitating easy exploration and enhanced search capabilities for users. Key performance indicators (KPIs) monitored throughout the process include the number of entities extracted, processing time, and the accuracy of the classifications. The business value of this DAG lies in its ability to provide a structured and enriched dataset that supports better decision-making, improves customer experiences through enhanced search functionalities, and drives operational efficiencies within the retail sector.

Part of the Knowledge Portal & Ontologies solution for the Retail industry.

Use cases

• Improves data organization for better decision-making
• Enhances customer experience with improved search functionalities
• Drives operational efficiencies through structured data access
• Facilitates quick identification of key market trends
• Supports data-driven strategies in retail operations