Semantic Graph Embedding via Markov Chain Distance Vectors
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Solution Overview
Problem
Current semantic embedding techniques face challenges in representing discrete data in a continuous semantic space and understanding the context-dependent meanings of data, particularly in natural language processing and image processing, where existing methods fail to accurately capture the nuances of data usage across different contexts.
Innovation Solution
The development of a semantic graph embedding system using Markov chains and graph length embedding, which constructs a continuous semantic space by generating a Markov chain from discrete data, computes arithmetic operations, and selects basis nodes to determine distances between nodes, allowing for the representation of context-dependent meanings through vector embeddings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If discrete data is represented using traditional semantic embedding techniques, then the data can be mapped to vectors, but the continuous semantic space cannot be effectively constructed and context-dependent meanings cannot be accurately captured
Solution Approach 1:
The patent transitions from discrete vector representations to a continuous semantic space by embedding graphs into a multidimensional continuous space. This dimensional transformation enables the system to capture context-dependent meanings and nuances that are lost in traditional discrete embedding approaches, resolving the contradiction between representation accuracy and context adaptability.
Solution Approach 2:
The patent changes the fundamental parameter of data representation from discrete vectors to continuous values in a semantic space. By transforming the representation domain from discrete to continuous, the system achieves both accurate semantic mapping and the ability to understand context-dependent variations in data meaning.
2Productivity
If existing semantic embedding methods are used, then words or phrases can be represented as vectors, but the nuances of data usage across different contexts cannot be captured
Solution Approach 1:
The patent introduces dynamic context-aware representations by constructing semantic graphs that capture relationships between data points. This dynamic approach allows the system to adapt vector representations based on contextual relationships, thereby improving contextual meaning accuracy while maintaining processing efficiency through automated graph construction and embedding.
Solution Approach 2:
The patent uses semantic graphs as an intermediary structure between raw discrete data and final vector representations. This intermediate semantic graph layer captures contextual relationships and nuances, serving as a mediator that transforms simple word/phrase vectors into context-aware continuous representations without sacrificing processing efficiency.
3Adaptability or versatility
If a continuous semantic space is constructed from discrete data, then context-dependent meanings can be represented, but the complexity of the embedding system increases
Solution Approach 1:
The patent segments the complex task of continuous semantic space construction into manageable components: (1) constructing semantic graphs from discrete data, (2) defining basis nodes and computing distances, and (3) generating vector embeddings. This segmentation reduces the perceived system complexity by breaking down the embedding process into discrete, implementable steps while maintaining the ability to represent context-dependent meanings.
Data Source
AI summary
Systems and method herein describe embedding graphs into a semantic multidimensional space by receiving a dataset, constructing a Markov chain from the dataset, the Markov chain comprising a plurality of nodes selecting a basis node and a target node from the plurality of nodes, determining a distance from the target node to the basis node, storing the distance between the target node and the basis node in a vector, analyzing the vector, and determining a semantic characteristic of the target node based on the analysis of the vector.