Reasonable Language Model Learning for Knowledge Graph Text Generation
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Solution Overview
Problem
Existing machine learning models face challenges in learning from limited, noisy, or unlabeled data, particularly in generating coherent text from knowledge graphs, where acquiring large-scale labeled datasets is tedious and impractical.
Innovation Solution
The proposed solution involves training a reasonable language model using triples and associated candidate labels generated from knowledge graphs and a large text corpus, enabling text generation for new triples.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional machine learning models are used for text generation from knowledge graphs, then the model requires large-scale labeled datasets for training, but acquiring such datasets is tedious and impractical
Solution Approach 1:
The system performs self-service by automatically generating training data from the knowledge graph structure itself. The model learns to generate text by utilizing the inherent relationships and entities within the knowledge graph, without requiring external labeled datasets. This self-supervised approach eliminates the tedious data acquisition process while maintaining training effectiveness.
Solution Approach 2:
The knowledge graph serves as an intermediary between the training data and the text generation task. Instead of requiring external labeled corpora, the system uses the structured knowledge graph as a mediator to provide the necessary training signals, transforming graph structures into language generation tasks automatically.
2Ease of manufacture
If machine learning models are trained with limited and noisy data, then the training process becomes more practical, but the model's learning effectiveness deteriorates
Solution Approach 1:
The system converts the potential harm of noisy and limited data into a benefit by designing a training objective that inherently filters for quality. The contrastive learning framework automatically distinguishes between correct and incorrect text generations, turning noise into a signal for learning robust representations. The model learns to identify correct relationships even when surrounded by incorrect alternatives.
Solution Approach 2:
The system changes the parameter of data quality requirements by introducing a contrastive learning objective that works effectively with limited data. Instead of requiring high-quality labeled data, the approach modifies the learning parameter to utilize unlabelled or minimally labeled data through contrastive objectives that leverage the knowledge graph structure.
3Reliability
If large-scale labeled datasets are used for training, then the model achieves better text generation performance, but the complexity and cost of data preparation increases
Solution Approach 1:
The system eliminates complex data preparation by making the knowledge graph self-sufficient for training. The structured relationships within the knowledge graph automatically provide the training signals needed, removing the need for manual annotation processes and complex data curation pipelines that would otherwise be required to achieve comparable performance.
Data Source
AI summary
Embodiments are provided for generating a reasonable language model learning for text data in a knowledge graph in a computing system by a processor. One or more data sources and one or more triples may be analyzed from a knowledge graph. Training data having one or more candidate labels associated with one or more of the triples may be generated. One or more reasonable language models may be trained based on the training data.