Joint Learning Model for Knowledge Graph and Natural Language Representation
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Solution Overview
Problem
Current representation learning methods for knowledge graphs and natural languages are independent and result in poor accuracy when used for text processing, as they do not effectively combine the co-occurrence laws between points and edges in knowledge graphs and words or sentences, leading to suboptimal semantic representations.
Innovation Solution
A joint learning model is developed that combines knowledge graph representation learning and natural language representation learning, incorporating a correlation layer to integrate knowledge graph and natural language learning layers, enabling the generation of improved semantic representations by considering multiple weights and topological structures for better text processing accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If knowledge graph representation learning and natural language representation learning are performed independently, then the complexity of the system is reduced, but the accuracy of text processing deteriorates
Solution Approach 1:
The patent merges knowledge graph representation learning and natural language representation learning into a unified joint learning model. The model integrates the knowledge graph learning layer and natural language learning layer through a correlation layer, allowing both learning processes to occur simultaneously and interactively, thereby improving text processing accuracy while maintaining manageable system complexity through unified architecture design.
2Measurement precision
If a joint learning model combining knowledge graph and natural language learning is used, then the quality of semantic representations is improved, but the device complexity increases
Solution Approach 1:
The joint learning model is segmented into distinct functional layers: a knowledge graph learning layer for processing knowledge graph data, a natural language learning layer for processing text data, and a correlation layer for integrating the two. This segmentation allows each layer to specialize in specific tasks while maintaining clear interfaces, improving semantic representation quality without overwhelming system complexity.
Solution Approach 2:
The correlation layer serves as an intermediary between the knowledge graph learning layer and the natural language learning layer. It computes correlation weights and integrates information from both layers, enabling effective interaction and information sharing. This intermediary structure facilitates high-quality semantic representations by systematically combining knowledge from both sources without creating excessive complexity.
Data Source
AI summary
The present application discloses a text processing method and device based on natural language processing and a knowledge graph, and relates to the in-depth field of artificial intelligence technology. A specific implementation is: an electronic device uses a joint learning model to obtain a semantic representation, which is obtained by the joint learning model by combining knowledge graph representation learning and natural language representation learning, it combines a knowledge graph representation learning and a natural language representation learning, compared to using only the knowledge graph representation learning or the natural language representation learning to learn semantic representation of a prediction object, factors considered by the joint learning model are more in quantity and comprehensiveness, so accuracy of semantic representation can be improved, and thus accuracy of text processing can be improved.