Context-Aware Vector Representation for Location Data Entity Resolution
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Solution Overview
Problem
Mapping and navigation service providers face challenges in reconciling differences between customer/user location data and service provider location data, such as variations in ontology and format, making it difficult to combine these data sources for location-based services.
Innovation Solution
A method involving the generation of context-aware vector representations of location entities from different data sources using machine learning models, allowing for classification and combination of these entities into a new database, along with the prediction of new relationships between location entities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If customer location data and service provider location data are combined directly, then the quantity of location data increases, but the data quality and accuracy deteriorate due to differences in ontology and format
Solution Approach 1:
The patent introduces an intermediary entity resolution system that acts as a mediator between customer location data and service provider location data. This system uses machine learning models to transform and align data from different sources into a unified format, resolving ontological differences before combining the datasets. The intermediary processing layer ensures that data from heterogeneous sources can be integrated without sacrificing accuracy.
Solution Approach 2:
The patent applies parameter changes by transforming location data through various processing parameters including entity identification, attribute mapping, and confidence scoring. The system dynamically adjusts transformation parameters based on data source characteristics, enabling flexible adaptation to different ontologies and formats while maintaining data quality standards during the integration process.
2Measurement precision
If machine learning models are used to process and classify location entities, then the accuracy of entity matching improves, but the complexity of the system increases
Solution Approach 1:
The patent segments the complex entity resolution task into multiple independent machine learning models, each specializing in specific aspects such as entity identification, attribute matching, and relationship prediction. This modular segmentation allows each model to be optimized for its specific function while reducing the overall complexity compared to a single monolithic system. The segmented models can be independently trained, deployed, and maintained.
Solution Approach 2:
The patent implements universal machine learning models that perform multiple functions within a single framework. The entity resolution system uses multi-functional models that can simultaneously handle entity identification, attribute matching, and relationship prediction across different data sources. This multi-functionality reduces system complexity by consolidating multiple specialized components into unified models.
3Loss of information
If context-aware vector representations are generated for location entities, then the ability to discover new relationships improves, but the computational resources required increase
Solution Approach 1:
The patent applies partial action by generating context-aware vector representations selectively for entities that require relationship discovery, rather than processing all location data uniformly. The system identifies high-value targets for relationship prediction and applies computationally intensive vector generation only to those cases, reducing overall computational resource consumption while maintaining information completeness for critical relationships.
Data Source
AI summary
An approach is provided for combining location data sources. The approach, for instance, involves generating a first context-aware vector representation of a first location entity in a first data source and a second context-aware vector representation of a second location entity in a second data source. The approach also comprises processing the first context-aware vector representation and the second context-aware vector representation using a machine learning model to perform a classification of the first location entity as the same as the second location entity. The approach further comprises combining the first data source and the data source into a new database based on the classification and providing the new database as an output.