Relation-Enhanced Knowledge Graph Embedding for Complex Relations
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Solution Overview
Problem
Existing knowledge graph embedding methods are limited by their entity-relation perspective, leading to chaotic entity mapping and inaccurate prediction, especially in scenarios involving N-to-N relations, and fail to consider importance levels of entities, resulting in poor reasoning and incomplete topological structure information.
Innovation Solution
A relation-enhancement knowledge graph embedding method that performs collaborative coordinate-transformation, relation core enhancement through entropy weighting, and builds an interpretability mechanism, using dynamic parameter-adjusting strategies to ensure accurate embedding and representation learning, with a weight matrix added to the score function for improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If existing knowledge graph embedding methods use entity-relation perspective, then the model structure is simple, but the entity mapping becomes chaotic and prediction accuracy deteriorates
Solution Approach 1:
The patent segments the embedding process into distinct modules: coordinate transformation module that converts entity coordinates, relation enhancement module that applies entropy weighting, and score calculation module that computes prediction scores. This segmentation allows each module to specialize in specific tasks, improving overall prediction accuracy while maintaining manageable model complexity through modular architecture.
Solution Approach 2:
The patent introduces coordinate transformation that operates in a multi-dimensional space, converting entity coordinates from original space to transformed space where relations can be better represented. This dimensional transformation enables more accurate capture of complex relation patterns without requiring excessive model complexity.
2Device complexity
If existing methods use single entity-relation link, then the model is simple, but the entity mapping becomes chaotic preventing accurate link prediction
Solution Approach 1:
The patent introduces coordinate transformation as an intermediary step between entity representation and relation prediction. This intermediary transformation converts entity coordinates into a space where relation patterns are more discernible, serving as a mediator that bridges the gap between simple entity-relation links and accurate prediction requirements.
Solution Approach 2:
The patent applies entropy weighting as a parameter change mechanism that dynamically adjusts the importance of different relation dimensions. By changing the weighting parameters based on entropy calculations, the model can adaptively emphasize critical relation features while downweighting less important ones, improving link prediction reliability without substantially increasing model complexity.
3Device complexity
If existing models lack interpretability, then the model can be compact, but the black box nature confuses researchers and reduces trust
Solution Approach 1:
The patent incorporates interpretability mechanisms that provide feedback on the embedding process. The score function calculates and returns prediction scores that can be analyzed to understand model behavior. Additionally, the entropy weighting provides feedback on which relation dimensions are most important, enabling researchers to interpret the model's decision-making process without requiring complex architectural changes.
4Adaptability or versatility
If knowledge graphs are updated frequently, then the knowledge base remains current, but the closed world assumption prevents prediction of new entities and relations
Solution Approach 1:
The patent performs preliminary coordinate transformation and relation enhancement on existing knowledge graph data before making predictions. By pre-processing the data through these transformation layers, the model creates a more flexible representation that can better adapt to new entities and relations when the knowledge graph is updated, reducing the need for complex restructuring while maintaining adaptability.
Data Source
AI summary
The present invention relates to a relation-enhancement knowledge graph embedding method and system, wherein the method at least comprises: performing collaborative coordinate-transformation on entities in the knowledge graph; performing relation core enhancement by means of relation-entropy weighting, so as to endow entity vectors with strong relation property; building an interpretability mechanism for a knowledge graph embedding model, and accounting for effectiveness and feasibility of the relation enhancement by proving convergence of the knowledge graph embedding model; and using a dynamic parameter-adjusting strategy to perform learn representation learning of to the vectors in the knowledge graph, and configuring deviation control to ensure accurate embedding. The present invention can measure rationality of facts with improved accuracy, prove through reasoning the modeling ability of the model from the perspective of complex relation pairs, perform vector computing for entities and relations, thereby accomplishes knowledge graph embedding and reasoning.