Machine Learning Model for Structured Data Extraction
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Solution Overview
Problem
The high cost and time-consuming process of generating structured data for search engines, particularly for multiple domains like sports, wrestling, football, and basketball, due to the need for manual effort and subscription fees from structured data providers, limits the availability and flexibility of user experiences.
Innovation Solution
Extracting and processing unstructured information from articles and other sources using a trained machine learning model to identify and store relevant entities as structured content, allowing for the generation of user interfaces without relying on a single structured data provider, thus avoiding manual creation and subscription fees.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If structured data is obtained from structured data providers, then data quality and reliability are improved, but cost and dependency increase
Solution Approach 1:
The system creates structured data copies from unstructured content by training machine learning models on available unstructured data sources. The models learn to extract and structure information independently, producing structured data that mirrors the quality of provider-based data without the dependency on specific providers.
Solution Approach 2:
The system enables self-service by automatically generating structured data from unstructured sources through machine learning models. Instead of relying on external providers to create and maintain structured data, the system autonomously performs extraction, validation, and structuring of information from multiple unstructured sources.
2Manufacturing precision
If manual effort is used to create structured content, then data accuracy is improved, but time and expense increase
Solution Approach 1:
The system replaces manual mechanical processes with automated machine learning models. The models are trained on unstructured data and automatically perform extraction, validation, and structuring tasks that previously required human reviewers, thereby maintaining accuracy while dramatically increasing productivity.
Solution Approach 2:
The system performs preliminary action by pre-training machine learning models on large volumes of unstructured data before deployment. This pre-training enables the models to automatically recognize patterns and extract structured information with high accuracy without requiring manual intervention for each data point.
3Adaptability or versatility
If multiple data sources are utilized, then data availability is improved, but processing complexity increases
Solution Approach 1:
The system implements universality by designing machine learning models that can process multiple types of unstructured data sources (articles, transcripts, social media posts, etc.) through a unified framework. The models are trained to handle diverse formats and sources simultaneously, reducing processing complexity while maintaining data availability across multiple sources.
Data Source
AI summary
Aspects of the present disclosure are directed to providing a rich content experience based on information received from unstructured content. A plurality of information items may be obtained from a plurality of data source, where each information item includes unstructured content. The plurality of information items may be provided to a trained machine learning model, where the model is trained with training data that includes information items and corresponding labeled entities for a plurality of historical events. In examples, a formatted request may be received, where the formatted request is associated with one or more labeled entities associated with the trained machine learning model. The trained machine learning model may identify multiple entities from the unstructured content based on the formatted request associated with the one or more labeled entities. In examples, each identified entity of the multiple identified entities is stored as structured content responsive to the formatted request.