AI Link Resolution Using Pre-Seeded Models and Query Expansion
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Artificial intelligence models face challenges in accurately linking entities across diverse and complex organizational networks due to ambiguity in entity naming, volume and diversity of data, incomplete or erroneous data, and the need for dynamic and scalable performance.
Innovation Solution
Utilizing pre-seeded training data and a novel AI model architecture involving two AI models to map entities to ranked documentation, with a language model generating additional queries for efficient link resolution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional entity resolution methods are used, then the system can process data, but the accuracy is insufficient due to ambiguity in entity naming and data diversity
Solution Approach 1:
The system segments the entity resolution task into multiple specialized AI models, each handling specific aspects of the problem. The first model focuses on entity identification and disambiguation, while the second model handles link resolution and documentation mapping. This segmentation allows each model to be optimized for its specific function, improving overall accuracy without requiring a single overly complex model.
Solution Approach 2:
The system performs preliminary actions by pre-processing and pre-seeding training data before the main entity resolution task. This includes preparing curated training datasets, establishing entity schemas, and pre-training models on relevant data. These preliminary steps enhance the models' ability to handle ambiguous entity names and diverse data formats, improving link resolution accuracy.
2Measurement precision
If sophisticated NLP techniques and contextual understanding are applied, then link resolution accuracy improves, but resource consumption increases
Solution Approach 1:
The system applies partial action by using multiple specialized models rather than a single comprehensive model. Each model performs a specific function (entity identification, disambiguation, link resolution), allowing the system to achieve high accuracy through coordinated specialized components rather than overwhelming resource consumption by a single large model.
Solution Approach 2:
The system introduces intermediary components including curated training datasets and pre-processing layers that bridge the gap between raw data and the main resolution models. These intermediaries prepare and structure data before it reaches the resolution models, reducing their computational burden while maintaining high accuracy through quality-controlled intermediate representations.
3Reliability
If the model processes large volumes of diverse documents, then comprehensive link resolution is achieved, but processing speed and scalability are reduced
Solution Approach 1:
The system segments the large volume of diverse documents into manageable processing streams handled by specialized models. Each model processes specific types of entities or documents in parallel, enabling comprehensive link resolution across all documents while maintaining processing speed through distributed specialized computation rather than sequential general processing.
4Measurement precision
If the entity resolution system is made dynamic and adaptive to new data, then accuracy is maintained, but system complexity increases
Solution Approach 1:
The system implements feedback mechanisms where the entity resolution models continuously learn from their outputs and new data. The models receive feedback on their link resolution decisions and use this to refine their parameters and improve future performance. This feedback loop maintains high accuracy while adapting to new entities and documentation formats without requiring complete system redesign.
Data Source
AI summary
Systems and methods for an artificial intelligence model architecture that involves a first artificial intelligence model trained to map a plurality of entities to ranked documentation from a documentation source, and a second artificial intelligence model that comprises a language model trained to generate an additional query to run on the plurality of documents from the documentation source. By training the second model to generate additional queries as entities and/or links are discovered, the system may quickly and efficiently determine links and/or potential resolutions as well as received feedback thereon.