Knowledge Graph Retrieval Using Chunked Context Windows
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Solution Overview
Problem
Existing NLP models face challenges in accurately capturing complex relationships between elements across documents due to the context window problem, which involves selecting relevant information and managing context window size, leading to increased latency and resource intensity, especially when dealing with multi-modal documents and linked resources.
Innovation Solution
A semantic network platform that segments documents into content chunks, translates non-textual data, and generates node triples in a knowledge graph, using techniques like concept proximity analysis and translation pre-processing to integrate diverse data types, enabling efficient and scalable knowledge retrieval.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If NLP models analyze and understand human language to enable semantic search capabilities, then search accuracy and relevance are improved, but computational resources and processing time are increased
Solution Approach 1:
The patent segments documents into smaller chunks or blocks before processing them through NLP models. This division reduces the context window size required for each processing operation, thereby lowering computational resource consumption while maintaining search accuracy through hierarchical processing of segmented content
Solution Approach 2:
The patent implements pre-processing steps including document chunking, embedding generation, and knowledge graph construction before actual search queries. These preliminary actions organize and pre-compute data structures, reducing the computational burden during runtime search operations and enabling faster, more efficient query processing
2Adaptability or versatility
If NLP models are trained on vast amounts of text data to learn language nuances, then semantic understanding capability is improved, but training time and computational cost are increased
Solution Approach 1:
The patent pre-trains NLP models on comprehensive text data to learn language nuances, syntax, and semantics before deployment. This preliminary training action establishes robust semantic understanding capabilities that can be reused across multiple search operations, avoiding repeated training and reducing overall training time impact
Solution Approach 2:
The patent uses pre-trained language models where the trained model weights and parameters are copied and deployed for various semantic search tasks. This allows the system to leverage previously learned language nuances without retraining, significantly reducing training time while maintaining high semantic understanding capability
3Measurement precision
If the context window size is increased to capture complex relationships across documents, then relationship detection accuracy is improved, but processing speed and resource efficiency are reduced
Solution Approach 1:
The patent segments large documents into smaller chunks that can be processed within manageable context windows. By organizing content into hierarchical segments and using knowledge graphs to represent relationships between segments, the system maintains relationship detection accuracy while reducing the computational burden of processing entire documents at once
Solution Approach 2:
The patent introduces knowledge graphs as intermediary structures that capture relationships between document chunks, concepts, and entities. These knowledge graphs serve as mediators that encode complex relationships in a compressed format, allowing the system to detect relationships accurately without requiring the full context window to contain all related information simultaneously
4Loss of information
If multi-modal documents and linked resources are processed to provide comprehensive search results, then information completeness is improved, but system complexity and processing overhead are increased
Solution Approach 1:
The patent implements a unified processing framework that handles multiple document types (text, images, videos, audio) and linked resources through common NLP pipelines. By translating non-textual data into textual representations and using universal embedding models, the system achieves information completeness across modalities without requiring separate complex processing systems for each data type
Data Source
AI summary
A resource processor segments a resource into content chunks based on dynamically identified boundaries and uses the content chunks to determine a first concept, a second concept, and a relationship, such as by performing concept proximity analysis and/or by analyzing resource metadata. A node generator generates and causes execution of computer-executable code to generate a node triple in a graph structure, the node triple including a first node encoding the first concept, a second node encoding the second concept, and a relationship node. A node indexer uses the node triple to generate embeddings, which are indexed relationally to a first node identifier that correlates to the first concept. An interface module queries the embeddings to identify embeddings associated with the first node identifier that correlates to at least a portion of a query. Updates to the resource cause incremental updates in portions of related nodes and related embeddings.