Semantic Concept Synthesis for Scalable Knowledge Coherence
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Solution Overview
Problem
Current systems face challenges in scaling knowledge representations due to technical complexity and precision, limiting the generation and use of new knowledge beyond the bounded knowledge encapsulated in existing documents, and lack a mechanism to automate the creation of new concepts and relationships.
Innovation Solution
A computer-implemented method and system that synthesizes concept definitions and relationships by extracting real concept definitions from a domain, analyzing them for coherence, and deriving virtual concept definitions through semantic processing to form a hierarchical structure, using protocols like formal concept analysis and faceted classification synthesis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If knowledge representation systems are expanded to include more concepts and relationships, then the breadth of knowledge increases, but the technical complexity and precision requirements increase, creating barriers to scale
Solution Approach 1:
The patent segments the knowledge representation system into multiple hierarchical levels (e.g., concept, attribute, value, instance). This segmentation allows the system to manage complexity by organizing knowledge into manageable units that can be independently processed and scaled, resolving the contradiction between knowledge breadth and technical complexity
Solution Approach 2:
The patent introduces hierarchical dimensions to organize knowledge representations, adding structural depth to the knowledge system. By organizing concepts across multiple hierarchical levels rather than in a flat structure, the system can expand knowledge breadth while maintaining manageable complexity through hierarchical abstraction
2Loss of information
If existing document-based knowledge extraction methods are used, then the system can retrieve knowledge from documents, but the knowledge is limited to what is explicitly captured in the existing forms and cannot generate new knowledge
Solution Approach 1:
The patent performs preliminary semantic processing and concept extraction from documents to build structured knowledge representations before actual knowledge queries are made. This preliminary structuring enables the system to not only retrieve existing knowledge but also to generate new knowledge by combining and reasoning over the pre-processed conceptual structures
Solution Approach 2:
The patent introduces semantic concepts and hierarchical structures as intermediary representations between raw documents and knowledge queries. These intermediaries enable the system to transform unstructured document content into structured knowledge that can be combined and reasoned over to generate new knowledge, bridging the gap between document retrieval and knowledge creation
3Manufacturing precision
If manual preparation of knowledge representations is performed to ensure technical complexity and precision, then the quality of knowledge representation improves, but the time and intellectual constraints limit their generation and use
Solution Approach 1:
The patent implements automated systems that perform semantic processing, concept extraction, and hierarchical organization of knowledge representations without requiring extensive manual preparation. The system serves itself by automatically structuring and organizing knowledge from documents, ensuring precision through algorithmic processing while eliminating time constraints associated with manual knowledge representation creation
Data Source
AI summary
A method for assessing the coherence of an input with a data processing system using synthesized concepts is provided. The method includes obtaining an active concept definition from the input of a cognitive agent, extracting real concept definitions composed of a set of attributes from an analyzed domain, matching the active concept definition to the extracted definitions, deriving virtual concept definitions from the real concept definitions using a semantic processing protocol such that the derived virtual concept definitions form a tree-structure graph of concepts and concept relationships, and measuring the attribute set coherence of the virtual concept definitions using a confidence gradient. The confidence gradient is based on at least one metric of relative proximity and co-occurrence. The method further includes assessing the probability of coherence, of the input with the data processing system, based on the measure of coherence within the confidence gradient.