Entigen-Based Knowledge Representation for Semantic Query Resolution
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Solution Overview
Problem
Current technologies are unable to comprehend the meaning of text data, leading to inefficiencies in extracting relevant information from large datasets, as they rely on word-based pattern matching and statistical reasoning, failing to understand the context and descriptive purposes of words, which limits their ability to provide insightful knowledge.
Innovation Solution
The proposed approach uses a new theory of sets-logic-and-language, represented by 'Entigens' and 'Identigens', which uniquely symbolize actions, items, and attributes, allowing computers to comprehend words in context and represent knowledge accurately, eliminating grammatical and statistical complexities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If word-based pattern matching and statistical reasoning are used to process text data, then processing speed can be maintained at computer levels, but the system cannot comprehend the meaning, context, or descriptive purposes of words
Solution Approach 1:
The patent introduces an intermediary layer between raw text data and computer processing. This intermediary system uses natural language processing techniques, including entity recognition, relationship extraction, and semantic analysis, to transform unstructured text into structured knowledge representations that computers can comprehend while preserving the original meaning and context
Solution Approach 2:
The patent replaces traditional mechanical word-matching systems with advanced semantic understanding systems. Instead of relying on simple pattern matching and statistical probability, the system uses knowledge graphs, ontologies, and contextual analysis to achieve genuine comprehension of text meaning, enabling computers to understand not just what words are present but what they mean in context
2Quantity of substance
If the volume of data stored in information systems increases, then more information is available for potential analysis, but the ability to extract relevant information efficiently decreases due to the overwhelming volume
Solution Approach 1:
The patent applies extraction principles by selectively pulling out relevant information from massive data volumes. The system identifies and extracts key entities, relationships, and semantic elements from unstructured text, separating signal from noise. This extraction process creates condensed knowledge representations that can be efficiently queried and analyzed without processing the entire raw data volume
Solution Approach 2:
The patent creates simplified copies or representations of complex text data. Instead of processing the original overwhelming volume of raw text, the system generates compressed knowledge graphs, structured databases, and semantic models that replicate the essential information in a computer-friendly format, enabling efficient retrieval and analysis
3Ease of operation
If traditional search-and-retrieve operations driven by word-based pattern matching are used, then the system can return explicit answers when they exist in the database, but it cannot provide insightful knowledge or understand implicit information
Solution Approach 1:
The patent performs preliminary action by pre-processing and pre-structuring text data into comprehensive knowledge representations before queries are submitted. The system预先 builds knowledge graphs, extracts relationships, and organizes information in a way that enables both simple explicit answer retrieval and complex implicit knowledge discovery from the same prepared structure
Data Source
AI summary
A method includes obtaining a sequence of words of a query and identifying, for each word of the sequence of words, an identigen subset to produce a plurality of identigen subsets. The method further includes determining, in accordance with grouping rules, a query entigen grouping that includes a sequence of unique entigens, where an entigen of the sequence of unique entigens corresponds to an identigen of a corresponding identigen subset of the plurality of identigen subsets, and where the query entigen grouping corresponds to a meaning of associated with the query. The method further includes identifying a section of a knowledge base that substantially matches the query entigen grouping, where the knowledge base facilitates storage of knowledge as a multitude of entigen groupings. The method further includes determining an answer to the query based on the section of the knowledge base.