Symbolic Word Sentence Processing for Knowledge Retrieval
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Solution Overview
Problem
Conventional help desk systems and search engines lack the capability to store and retrieve user-specific knowledge using English language sentences, synonyms, and symbol words, leading to inefficient data retrieval and misinterpretation of user queries.
Innovation Solution
A software system utilizing Prolog programming to process English language sentences, allowing users to store custom knowledge with methods for retrieving it using words, synonyms, and symbol words, enabling users to link sentences to customizable attachments and query search engines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional help desk systems and search engines are used, then data retrieval is performed, but the capability to store and retrieve user-specific knowledge using English language sentences, synonyms, and symbol words is lacking, leading to inefficient data retrieval and misinterpretation of user queries
Solution Approach 1:
The system transforms the parameter of data representation from conventional indexed terms to symbolic words and English language sentences. By changing how knowledge is stored and queried (using symbolic representation and natural language processing), the system achieves both accurate retrieval and enhanced adaptability to user queries with synonyms and symbolic terms.
Solution Approach 2:
The patent introduces an intermediary layer of symbolic words and synonym mappings between user queries and the knowledge base. This intermediary structure enables the system to interpret various forms of user input (including synonyms and symbolic terms) and accurately retrieve relevant knowledge, resolving the contradiction between retrieval accuracy and linguistic adaptability.
2Reliability
If users store custom knowledge with English language sentences and link to attachments, then knowledge retrieval accuracy improves, but the system complexity increases
Solution Approach 1:
The system segments knowledge storage into distinct components: English language sentences, symbolic words, synonym mappings, and attachment links. This segmentation allows each component to be managed independently, improving knowledge retrieval quality while organizing system complexity into manageable modular units that can be processed separately.
Solution Approach 2:
The system performs preliminary processing of knowledge during the storage phase by establishing symbolic word mappings, synonym relationships, and attachment associations before retrieval is needed. This preliminary structuring of data enables accurate and reliable search results while reducing the computational complexity during actual query processing, as the heavy lifting of organization is done in advance.
3Measurement precision
If multiple data sources are integrated, then knowledge quality and relevance improve, but the steps required to retrieve knowledge increase
Solution Approach 1:
The system creates a universal knowledge structure that can handle multiple data sources through a common framework of symbolic words, synonyms, and attachments. This multi-functional architecture allows the system to integrate diverse data sources while maintaining a consistent retrieval interface, improving result relevance without proportionally increasing retrieval steps.
Solution Approach 2:
The system implements feedback mechanisms where synonym mappings and symbolic word associations are continuously refined based on retrieval patterns. This feedback loop enables the system to learn from integration experiences across multiple data sources, optimizing the retrieval process over time to maintain high relevance while reducing the number of steps required.
Data Source
AI summary
A software system that automates the use of computing systems by storing English language sentences using synonyms and symbol words to allow user access to computer resources without having to know the technical details or the specifics associated with a body of knowledge. Synonym sentences allow users to get to computer resources using different words stored in different sentences that open the same computer resource (i.e.: a document) to significantly improve a first time request for information. Sentences learned (stored) in text or SQL databases or used in a software agent sentence or search engine sentence are automatically parsed and directed to the appropriate knowledge repository to automated computer processes or to get user information without having to know any technical details required to navigate through computer systems or computer data. Multiple words or sentences can be linked together with computer resources to further automate job tasks.