Consumer Products — Entity and Taxonomy Extraction Pipeline

Overview

The primary purpose of the Entity and Taxonomy Extraction Pipeline is to utilize Named Entity Recognition (NER) techniques to identify and extract key taxonomies and concepts from various consumer product corpora. The data sources include product descriptions, customer reviews, and market research reports, which are ingested into the system for processing. The ingestion pipeline begins with data collection from these sources, followed by preprocessing steps that clean and normalize the data to e

The primary purpose of the Entity and Taxonomy Extraction Pipeline is to utilize Named Entity Recognition (NER) techniques to identify and extract key taxonomies and concepts from various consumer product corpora. The data sources include product descriptions, customer reviews, and market research reports, which are ingested into the system for processing. The ingestion pipeline begins with data collection from these sources, followed by preprocessing steps that clean and normalize the data to ensure consistency. The core processing involves applying NER algorithms to identify relevant entities and taxonomies, which are then validated through quality control measures that assess extraction accuracy. Regular updates ensure that the knowledge graph remains current, reflecting the latest product information and trends. The outputs of this DAG include structured data sets that enhance the knowledge graph, providing enriched metadata for improved search capabilities. Key performance indicators (KPIs) for monitoring include extraction accuracy rates, the volume of entities processed, and the frequency of updates to the knowledge graph. The business value lies in enabling organizations to leverage structured data for better decision-making, enhancing customer insights, and improving product development strategies.

Part of the Knowledge Portal & Ontologies solution for the Consumer Products industry.

Use cases

• Improves data accessibility for product teams.
• Facilitates faster and more accurate market analysis.
• Enhances customer insights through enriched data.
• Supports agile product development with up-to-date information.
• Increases operational efficiency by automating data extraction.