Cross-media Knowledge Mapping via Spherical Feature Indexing
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Solution Overview
Problem
Current methods for cross-media knowledge representation require a large number of training samples and have low processing efficiency and limited accuracy.
Innovation Solution
A cross-media corresponding knowledge generation method that maps first and second knowledge units of different media types to a two-dimensional spherical feature surface, generating feature points which are then indexed to establish a bidirectional index corresponding relationship, enabling high-efficiency and high-accuracy cross-media knowledge representation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If model training method is used for cross-media knowledge representation, then knowledge representation can be achieved, but it requires a large number of training samples and has low processing efficiency and limited accuracy
Solution Approach 1:
The patent pre-establishes a bidirectional index corresponding relationship between knowledge units of different media types before actual knowledge representation tasks. This preliminary indexing structure allows direct mapping without requiring extensive training samples during operation, thereby improving accuracy while reducing the quantity of training data needed.
Solution Approach 2:
The patent introduces a bidirectional index as an intermediary structure that mediates between knowledge units of different media types. This index structure enables direct mapping and correspondence establishment without relying on traditional model training, thus achieving high accuracy with minimal training samples.
2Productivity
If model training method is used for cross-media knowledge representation, then knowledge representation can be achieved, but processing efficiency is low
Solution Approach 1:
The patent performs index construction and bidirectional correspondence establishment in advance, before actual knowledge representation tasks. This preliminary action eliminates the need for time-consuming model training during operation, significantly improving processing efficiency and reducing time loss.
Solution Approach 2:
The patent creates index copies and bidirectional mappings that can be directly used for knowledge representation without repeated training. This copying approach allows rapid retrieval and mapping operations, thereby improving productivity and reducing training time requirements.
3Measurement precision
If traditional cross-media knowledge mapping is used, then knowledge mapping between media can be achieved, but mapping accuracy is limited
Solution Approach 1:
The patent introduces a bidirectional index as an intermediary structure that enables accurate mapping between knowledge units of different media types. This index structure maintains clear correspondence relationships without requiring complex transformation models, thereby improving mapping accuracy while keeping system complexity manageable.
Solution Approach 2:
The patent changes the parameter representation by using bidirectional index correspondence instead of traditional model parameters. This parameter transformation enables more accurate knowledge mapping by directly establishing correspondence relationships between different media types without relying on complex trained parameters.
Data Source
AI summary
A method and an apparatus for cross-media corresponding knowledge generation. The method comprises: generating a second knowledge unit of a second medium according to a first knowledge unit of a predefined first medium; generating a first feature parameter vector corresponding to the first knowledge unit and a second feature parameter vector corresponding to the second knowledge unit; mapping the first feature parameter vector and the second feature parameter vector to a corresponding two-dimensional spherical feature surface to obtain a first feature point of the first feature parameter vector on the corresponding two-dimensional spherical feature surface and a second feature point of the second feature parameter vector on the corresponding two-dimensional spherical feature surface; indexing the first feature point and the second feature point to obtain a first index and a second index; and generating a bidirectional index corresponding relationship between the first knowledge unit and the second knowledge unit.