Embedding-Based Entity Resolution for Noisy Elasticsearch Data
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Solution Overview
Problem
Existing database resolution systems face challenges in handling non-static, duplicated, and noisy data, particularly in elastic search environments, leading to inefficient and resource-intensive manual user refinement and suboptimal query results.
Innovation Solution
A computer-implemented system utilizing natural language processing models to map and index entity records into an enhanced elastic search environment, employing a clustering engine to determine similarities and an ingestion module to automatically index and merge high-similarity records, reducing the need for manual user intervention and recursive searches.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional fuzzy search methods are used for database resolution, then the system can handle basic search queries, but the processing time increases and system load becomes excessive when dealing with tens of millions of records
Solution Approach 1:
The patent replaces traditional mechanical fuzzy search algorithms with a machine learning-based embedding model that uses neural networks to transform entity data into dense vector representations. This substitution enables the system to handle tens of millions of records efficiently by leveraging the semantic understanding capabilities of the ML model rather than relying on computationally intensive string matching operations.
Solution Approach 2:
The patent changes the fundamental parameter of data representation from traditional text strings to embedded vector representations. By transforming entity attributes into continuous vector space representations through the machine learning model, the system achieves O(1) lookup complexity for similar entities, dramatically reducing processing time from linear scanning to constant time retrieval.
2Measurement precision
If manual user refinement is used to resolve entity records, then the system can achieve accurate results, but the process becomes resource-intensive and requires significant user intervention
Solution Approach 1:
The patent implements self-service by enabling the system to automatically resolve entity records through machine learning-based clustering and similarity matching. The system autonomously processes entity data, identifies similar records, and merges duplicates without requiring manual user intervention, while maintaining high resolution accuracy through the intelligent embedding model.
Solution Approach 2:
The patent incorporates feedback mechanisms where the system continuously learns from user interactions and refines its embedding models. User corrections and preferences are fed back into the training process, allowing the system to improve its resolution accuracy over time while reducing the need for manual refinement.
3Adaptability or versatility
If data is stored in multiple databases with different arrangements, then the system can access diverse information sources, but the data becomes difficult to resolve and requires complex processing
Solution Approach 1:
The patent achieves universality by creating a unified embedding model that can process and represent entities from multiple diverse data sources and databases. The single machine learning model handles various data formats, schemas, and sources, transforming them all into a common vector representation space, thereby simplifying the complexity of multi-source data resolution.
Solution Approach 2:
The patent introduces an intermediary layer in the form of embedding vectors that mediates between diverse data sources and the resolution process. Instead of directly comparing heterogeneous data structures, the system transforms all source data into a common embedding space, serving as a universal intermediary representation that simplifies similarity computation across different databases.
Data Source
AI summary
This disclosure generally relates to computer-based systems configured for one or more novel technological applications of information processing in the field of database resolution from an enhanced query data refinement in an elastic search environment utilizing a machine learning model pipeline to resolve entity records.