Knowledge Representation Modification via ML Classifier Feedback
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Solution Overview
Problem
The sheer volume of digital content poses challenges in identifying information of interest to users efficiently, as existing methods require substantial manual labeling of content items for training machine learning classifiers, making it difficult to yield accurate results without overwhelming users with irrelevant information.
Innovation Solution
The system generates training data for machine learning classifiers by receiving knowledge representations based on objects of interest, determining scores for content items, and assigning labels using these representations, allowing for the classification of unlabeled content items and refinement of knowledge representations based on validation data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual labeling of content items is used to train machine learning classifiers, then classification accuracy can be improved, but the time and resources required for labeling increase significantly
Solution Approach 1:
The system performs preliminary action by using existing knowledge representations to pre-label content items before formal training. The knowledge representation system automatically generates initial labels based on conceptual relationships, which then serve as pre-prepared training data, eliminating the need for time-consuming manual labeling while maintaining classification accuracy.
2Quantity of substance
If more content items are provided to users, then information coverage is improved, but relevance to user interests deteriorates due to overwhelming volume
Solution Approach 1:
The system applies local quality by customizing content selection based on individual user interests and profiles. Instead of treating all users uniformly, the knowledge representation system adapts the labeling and filtering process to each user's specific preferences, ensuring that the content provided is both comprehensive and highly relevant to each user's particular interests.
3Measurement precision
If knowledge representations are extensively modified to improve classification, then classification accuracy improves, but system complexity increases
Solution Approach 1:
The system implements feedback by using classification results to iteratively refine the knowledge representation. The classification performance feeds back into the knowledge representation system, which automatically adjusts and modifies its structure based on actual classification outcomes, thereby improving accuracy without requiring manual intervention or complex system redesign.
Data Source
AI summary
Systems and methods are provided for modifying a knowledge representation based on a machine-learning classifier. The knowledge representation is synthesized based on an object of interest. The machine-learning classifier is applied to predict relevance of validation data items. The knowledge representation is modified based on the results of the machine-learning classifier and the validation data. The modified knowledge representation can be used in subsequent applications of the classifier.