High Tech — Key Entity and Taxonomy Extraction for Knowledge Enrichment

Overview

The purpose of this DAG is to streamline the extraction of key entities and taxonomies from new data inputs to enhance knowledge management within the high-tech industry. Triggered by the arrival of fresh data, the workflow employs Named Entity Recognition (NER) techniques to identify relevant entities and categorize them into structured taxonomies. The data sources include product descriptions, customer feedback, and technical documentation, which are ingested into the pipeline for processing.

The purpose of this DAG is to streamline the extraction of key entities and taxonomies from new data inputs to enhance knowledge management within the high-tech industry. Triggered by the arrival of fresh data, the workflow employs Named Entity Recognition (NER) techniques to identify relevant entities and categorize them into structured taxonomies. The data sources include product descriptions, customer feedback, and technical documentation, which are ingested into the pipeline for processing. The architecture consists of several processing steps: first, data ingestion occurs through automated data connectors; next, NER algorithms analyze the text to extract entities; subsequently, the identified entities are classified into predefined taxonomies. Quality control measures are implemented to validate the accuracy of the extracted data, ensuring that only high-quality entities are stored. Regular updates to the taxonomies are scheduled to reflect the evolving landscape of the high-tech industry. The outputs of this DAG are stored in a knowledge graph, facilitating enhanced search capabilities and access to critical information. Monitoring key performance indicators (KPIs) such as extraction accuracy and processing time ensures the effectiveness of the pipeline. The business value lies in improved knowledge management, enabling faster decision-making and fostering innovation through better access to relevant information.

Part of the SOPs & Playbooks solution for the High Tech industry.

Use cases

• Improved knowledge accessibility for faster decision-making
• Enhanced innovation through organized information retrieval
• Increased accuracy in entity extraction reduces errors
• Timely updates ensure relevance in a fast-paced industry
• Streamlined processes lead to operational efficiency