Ontology-Aided Relation Extraction for Intelligent Matching
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Solution Overview
Problem
Existing systems struggle to efficiently match service providers with customer requirements based on free-form text inputs, requiring manual intervention and lacking automated alignment with ontological structures.
Innovation Solution
A network-based intelligent matching system with a software-driven engine that utilizes text-to-ontology mapping to automatically extract structured information from free-form text, aligning service provider properties with user inputs, and scoring matches based on contextual ontologies and knowledge graphs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated text-to-ontology mapping is implemented, then productivity is improved, but device complexity increases
Solution Approach 1:
The patent introduces an intermediary layer (ontology and knowledge graph) between the free-form text input and the service provider matching system. This intermediary structure standardizes unstructured data into organized entities and relationships, enabling automated processing without requiring complex custom parsing logic for each matching scenario.
Solution Approach 2:
The system segments the complex text processing task into distinct modular components: named entity recognition module, relationship extraction module, ontology mapping module, and scoring module. Each component handles a specific aspect of the transformation from unstructured text to structured matching data, reducing overall system complexity.
2Productivity
If manual intervention is eliminated in the matching process, then productivity is improved, but measurement precision deteriorates
Solution Approach 1:
The system incorporates feedback mechanisms where the ontology structure and relationship patterns learned from successful matches are used to refine the scoring algorithm. The structured ontology provides continuous feedback on what constitutes a valid match, enabling the automated system to improve precision through iterative learning while maintaining high throughput.
Solution Approach 2:
The patent transforms the matching criteria from subjective manual evaluation parameters to objective quantifiable parameters based on ontology relationships and entity attributes. By changing the parameters to structured data elements (entity types, relationship strengths, attribute matches), the system achieves both automation and precision through consistent application of defined scoring rules.
Data Source
AI summary
An intelligent matching system network architecture with a software driven engine may establish one or more matches between properties attributed to an entity, for example, a service provider and a set of specified parameters, for example, the request properties of a customer. The system analyzes user provided free-form text to generate an automated match of requirements specified by a user and a prospective service provider. One or more service providers which are good matches for filling a specified set of parameters, for example, parameters specifying customer requirements or needs derived at least in part from user input are identified.