Neuro-symbolic Knowledge Base for Text Ambiguity Resolution

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Solution Overview

Problem

Existing computing systems face challenges in extracting useful information from large volumes of data due to issues like data accuracy and variations in how text is interpreted across languages and regional dialects.

Innovation Solution

The computing system employs a method that includes generating data representations of data, analyzing the data using these representations, and utilizing AI servers to ingest content, extract knowledge, and interact with user devices to provide responses to queries.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If pattern recognition techniques and statistical reasoning are used to process text, then the system can handle ambiguities in words, but the accuracy and reliability of information extraction deteriorates due to data volume and interpretation variances

Engineering Contradiction:

Improveability to handle text ambiguitiesVSAvoidaccuracy of information extraction

Core Design Contradiction:

Adaptability or versatilityVSReliability

Solution Approach 1:

The patent introduces an intermediary knowledge base that mediates between raw text data and the pattern recognition system. This knowledge base stores pre-processed, validated information about entities, relationships, and concepts, allowing the system to query for accurate interpretations rather than relying solely on statistical pattern matching, thus improving reliability while maintaining adaptability to text ambiguities

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The system performs preliminary action by pre-processing and validating text data before it enters the pattern recognition pipeline. The knowledge base is continuously updated with verified information, allowing the system to leverage pre-validated knowledge when processing new text, thereby improving extraction accuracy without sacrificing the ability to handle ambiguities

Inventive Principle:

Principle #10Preliminary action

2Quantity of substance

If the system processes large volumes of data, then it can find useful information, but the time required for processing increases

Engineering Contradiction:

Improvevolume of data processedVSAvoidprocessing time

Core Design Contradiction:

Quantity of substanceVSLoss of time

Solution Approach 1:

The patent segments the data processing task by dividing the knowledge base into manageable components (entities, relationships, attributes) and processing queries against these segmented structures. This allows the system to efficiently navigate large volumes of data by querying specific segments rather than scanning the entire dataset, reducing processing time while maintaining comprehensive data coverage

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system creates a copy of the knowledge base in memory that can be quickly queried without repeatedly scanning the original large dataset. This in-memory representation allows rapid retrieval of relevant information while the system continues to process and update the full data volume in the background, effectively decoupling query response time from data processing time

Inventive Principle:

Principle #26Copying

3Adaptability or versatility

If the system uses statistical reasoning to interpret text, then it can handle variations in language, but the precision of knowledge representation deteriorates

Engineering Contradiction:

Improvelanguage interpretation capabilityVSAvoidprecision of knowledge representation

Core Design Contradiction:

Adaptability or versatilityVSMeasurement precision

Solution Approach 1:

The patent employs a composite approach where statistical reasoning models are combined with structured knowledge representations. The system uses probabilistic language models to handle language variations while simultaneously maintaining precise symbolic representations of knowledge in the knowledge base, creating a hybrid system that achieves both adaptability to language and precision in knowledge representation

Inventive Principle:

Principle #40Composite materials

Solution Approach 2:

The system replaces pure statistical mechanical processing with a hybrid approach that incorporates symbolic knowledge representation. Instead of relying solely on statistical patterns, the system uses the knowledge base to provide precise, structured representations of entities and relationships, substituting the mechanical statistical process with a more precise symbolic framework while retaining the ability to handle language variations

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS20250148214A1Neuro-symbolic retrieval augmented generation hybrid approach to curating knowledge

Publication Date: 2025.05.08 ENTIGENLOGIC LLC

AI summary

A neuro-symbolic retrieval augmented generation hybrid method includes detecting an incomplete entigen group within a knowledge database. The incomplete entigen group includes entigens and one or more entigen relationships between at least some of the entigens. The incomplete entigen group represents knowledge of a topic. The method further includes obtaining additive content for the topic based on the incomplete entigen group and generating an additive entigen group based on the additive content. The method further includes updating the incomplete entigen group utilizing the additive entigen group to produce an updated entigen group. The method further includes indicating that the updated entigen group has an un-curated status when the additive entigen group conflicts with the incomplete entigen group.