Schema-Less Agricultural Data Ingestion Through Knowledge Graph Embeddings
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The challenge of unifying and normalizing agricultural data from multiple sources with varying schemas for crop yield prediction and diagnosis is complicated and often requires significant human intervention, leading to increased costs and inefficiencies.
Innovation Solution
A mapping model is used to generate relationships between agricultural attributes and nodes in a knowledge graph, allowing for automatic normalization of schema-less data, which includes projecting agricultural attributes into a shared embedding space and identifying corresponding nodes or adding new nodes when necessary, thereby enabling efficient data processing for machine learning tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual templates and human intervention are used to normalize agricultural data from multiple sources, then data unification accuracy is improved, but processing time and costs increase significantly
Solution Approach 1:
The patent replaces manual human intervention with an automated machine learning system that uses embedding models and knowledge graphs to normalize agricultural data. The system automatically maps attributes from different data sources to a unified schema without requiring manual template creation or human review, thereby eliminating the trade-off between accuracy and processing time.
Solution Approach 2:
The system performs self-service data normalization by automatically learning relationships between agricultural attributes from multiple sources and autonomously mapping them to a unified schema. The embedding model and knowledge graph work together to automatically resolve schema differences without external human assistance, enabling rapid processing while maintaining high accuracy.
2Measurement precision
If manual templates are created to handle schema-less agricultural data, then data normalization accuracy is improved, but computational resources and costs increase
Solution Approach 1:
The system performs preliminary action by pre-training embedding models on agricultural domain knowledge and pre-building knowledge graphs that encode relationships between agricultural attributes. This preliminary preparation enables the system to rapidly normalize new data sources without requiring costly manual template creation for each new data source, reducing both computational resources and costs while maintaining high accuracy.
Solution Approach 2:
The patent creates a universal normalization system using embedding models and knowledge graphs that can handle multiple different agricultural data sources with varying schemas through a single unified approach. This multi-functional system replaces the need for separate manual templates for each data source, significantly reducing computational resources and costs while maintaining consistent normalization accuracy across all sources.
3Productivity
If automated methods are used to process schema-less agricultural data, then processing efficiency is improved, but data unification accuracy may deteriorate
Solution Approach 1:
The patent introduces embedding spaces as an intermediary layer between raw agricultural data and the unified schema. The embedding model transforms attributes from different data sources into a shared vector space where semantic relationships are preserved, and the knowledge graph acts as an intermediary to guide the mapping process. This intermediary approach enables automated processing to achieve both high efficiency and high accuracy by bridging the gap between diverse input formats and the target unified schema.
Data Source
AI summary
Techniques are disclosed herein that enable generating a relationship embedding indicating a relationship between one or more agricultural attributes of a table of agricultural data with one or more nodes in an agricultural knowledge graph. Various implementations include processing a table of agricultural data with rows of agricultural records and columns of agricultural attributes. Additional or alternative implementations include processing the table of agricultural data using an embedding model portion of the mapping model to generate an embedding space representation of each of the agricultural attributes. Various implementations can include selecting a node corresponding to a given agricultural attribute based on a distance between the embedding space representation of the given agricultural attribute and the embedding space representations of one or more nodes.