Media — Knowledge Extraction from Unstructured Media Data

Overview

The primary purpose of this DAG is to extract actionable knowledge from unstructured media data, including articles, reviews, and social media content. By leveraging advanced techniques such as Named Entity Recognition (NER) and classification algorithms, the DAG transforms raw data into structured information that can be utilized for various analytical purposes. The ingestion pipeline begins with the collection of unstructured data from multiple sources, including online articles, user reviews,

The primary purpose of this DAG is to extract actionable knowledge from unstructured media data, including articles, reviews, and social media content. By leveraging advanced techniques such as Named Entity Recognition (NER) and classification algorithms, the DAG transforms raw data into structured information that can be utilized for various analytical purposes. The ingestion pipeline begins with the collection of unstructured data from multiple sources, including online articles, user reviews, and social media posts. Once ingested, the data undergoes a series of processing steps, where NER identifies relevant entities, and classification algorithms categorize the information based on predefined criteria. Quality controls are implemented to ensure the accuracy and relevance of the extracted knowledge, with monitoring mechanisms in place to track key performance indicators (KPIs) such as the volume of data processed and the precision of the extraction process. The final output is stored in a knowledge graph, enabling easy access and retrieval for further analysis. In case of any processing failures, a recovery mechanism is activated to ensure data integrity and continuity. This DAG not only enhances the understanding of media content but also provides valuable insights that can drive strategic decision-making within the media industry.

Part of the Scientific ML & Discovery solution for the Media industry.

Use cases

• Improves content discoverability and relevance for users.
• Enhances decision-making through structured insights.
• Reduces manual data processing efforts significantly.
• Increases operational efficiency in media analysis.
• Supports targeted marketing strategies based on insights.