Life Science — Taxonomy Extraction for Knowledge Management in Pharma

Overview

The purpose of this DAG is to streamline the extraction of taxonomies from scientific and regulatory documents within the life sciences sector. By leveraging advanced natural language processing (NLP) techniques, the workflow identifies key entities and their relationships, facilitating the organization of knowledge. The data sources for this process include scientific articles, regulatory filings, and internal standard operating procedures (SOPs). The ingestion pipeline begins with the collecti

The purpose of this DAG is to streamline the extraction of taxonomies from scientific and regulatory documents within the life sciences sector. By leveraging advanced natural language processing (NLP) techniques, the workflow identifies key entities and their relationships, facilitating the organization of knowledge. The data sources for this process include scientific articles, regulatory filings, and internal standard operating procedures (SOPs). The ingestion pipeline begins with the collection of these documents, followed by preprocessing steps such as text normalization and tokenization. The core processing steps involve entity recognition, relationship extraction, and taxonomy classification, which culminate in the construction of a comprehensive knowledge graph. Quality controls are implemented throughout the process, including validation checks to ensure that extracted information meets regulatory standards and is accurate. The outputs of this DAG include structured taxonomies, updated knowledge graphs, and reports on extraction quality metrics. Monitoring key performance indicators (KPIs) such as extraction accuracy and compliance rates is essential for assessing the effectiveness of the workflow. The business value of this DAG lies in its ability to enhance knowledge management capabilities, improve regulatory compliance, and streamline the update process for SOPs, ultimately leading to better decision-making and operational efficiency in the life sciences industry.

Part of the Knowledge Portal & Ontologies solution for the Life Science industry.

Use cases

• Improved efficiency in knowledge management processes
• Enhanced compliance with regulatory requirements
• Faster updates to SOPs based on extracted insights
• Increased accuracy in data-driven decision-making
• Streamlined access to critical scientific information