Media — Entity Extraction and Taxonomy Creation for Data Analysis

Overview

The primary purpose of this DAG is to extract significant entities from normalized media data and create a structured taxonomy that enhances data analysis and searchability. The workflow begins with ingesting various data sources, including metadata from media files, user interaction logs, and existing ontologies. The data ingestion pipeline employs Named Entity Recognition (NER) techniques to identify and extract relevant entities such as titles, genres, and key personnel from the input data. F

The primary purpose of this DAG is to extract significant entities from normalized media data and create a structured taxonomy that enhances data analysis and searchability. The workflow begins with ingesting various data sources, including metadata from media files, user interaction logs, and existing ontologies. The data ingestion pipeline employs Named Entity Recognition (NER) techniques to identify and extract relevant entities such as titles, genres, and key personnel from the input data. Following extraction, the DAG processes this information to update existing ontologies, ensuring that the taxonomy reflects the latest trends and relationships within the media landscape. Quality control measures are implemented throughout the process, including validation checks and consistency assessments, to guarantee the accuracy of the extracted entities. The final outputs include a comprehensive taxonomy and an updated knowledge graph that supports intuitive navigation and efficient querying capabilities. Monitoring key performance indicators (KPIs) such as extraction accuracy, processing time, and user engagement metrics provides insights into the effectiveness of the DAG. By streamlining entity extraction and taxonomy creation, this DAG delivers significant business value, enabling media organizations to enhance their data analysis capabilities and improve user experience in knowledge retrieval.

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

Use cases

• Enhances data analysis capabilities for media organizations
• Improves user experience in knowledge retrieval
• Supports real-time updates to media ontologies
• Enables better decision-making through structured data insights
• Increases operational efficiency by automating entity extraction