Public Sector — Regulatory Knowledge Extraction and Structuring Pipeline

Overview

The purpose of this DAG is to facilitate the extraction and structuring of regulatory knowledge specifically for the public sector, enabling efficient access to critical information. It ingests data from multiple sources, including regulatory documents, databases, and knowledge repositories. The ingestion pipeline employs Natural Language Processing (NLP) techniques, particularly Named Entity Recognition (NER) and taxonomic classification, to identify and categorize relevant regulatory informati

The purpose of this DAG is to facilitate the extraction and structuring of regulatory knowledge specifically for the public sector, enabling efficient access to critical information. It ingests data from multiple sources, including regulatory documents, databases, and knowledge repositories. The ingestion pipeline employs Natural Language Processing (NLP) techniques, particularly Named Entity Recognition (NER) and taxonomic classification, to identify and categorize relevant regulatory information. Once the data is extracted, it is organized into a knowledge graph, which enhances searchability and retrieval efficiency. Quality control measures are integral to the process, ensuring that the extracted data meets accuracy standards. In the event of data discrepancies or failures, a validation process is initiated to rectify errors, thus maintaining the integrity of the knowledge base. The outputs of this DAG include structured regulatory knowledge, which can be utilized for compliance checks, policy formulation, and decision-making processes within the public sector. Monitoring key performance indicators (KPIs) such as extraction accuracy, processing time, and user engagement with the knowledge graph provides insights into the effectiveness of the pipeline. The business value of this DAG lies in its ability to streamline regulatory compliance efforts, reduce time spent on information retrieval, and enhance decision-making capabilities for public sector organizations.

Part of the Predictive Maintenance solution for the Public Sector industry.

Use cases

• Improves regulatory compliance and reduces legal risks
• Enhances decision-making with accessible structured data
• Saves time in information retrieval and processing
• Increases stakeholder confidence in data accuracy
• Facilitates proactive policy development and implementation