Dynamic Knowledge Graph for Adaptive Query Response
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Solution Overview
Problem
Existing knowledge management systems fail to provide specific and optimal responses to user queries as they rely on fixed ontologies that do not consider external stimuli, leading to general answers rather than personalized responses.
Innovation Solution
A self-configuring dynamic knowledge base representation system that adjusts based on external events and meta-data, such as query origin, time, and environmental conditions, to provide user-specific answers by modeling knowledge representation as a graph with edge weights determined by conditional probabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a fixed ontology is used for knowledge representation, then the system structure is simple and stable, but the system cannot adapt to specific user queries and external stimuli
Solution Approach 1:
The patent implements a dynamic ontology that automatically reconfigures itself based on external stimuli and user queries. The knowledge representation transitions from a static structure to a dynamic one that adapts its topology, node activations, and edge weights according to the specific query context, resolving the contradiction between adaptability and complexity
Solution Approach 2:
The system changes parameters of the knowledge base representation including node activation states, edge weight values, and ontology topology based on external stimuli. This allows the same knowledge base to provide different levels of detail and focus for different users without requiring multiple fixed ontologies
2Measurement precision
If a fixed ontology is used for knowledge representation, then the system is easy to maintain, but it provides general answers rather than specific responses to user inquiries
Solution Approach 1:
The ontology performs self-configuration and self-optimization automatically in response to user queries and external stimuli. The system self-adjusts its knowledge representation without requiring manual intervention, maintaining answer specificity while preserving ease of operation through automated adaptation
3Measurement precision
If comprehensive knowledge analysis is performed to provide specific answers, then the answer quality improves, but the processing time increases
Solution Approach 1:
The system pre-configures the knowledge base ontology and pre-identifies relevant knowledge nodes and relationships before user queries are submitted. This preliminary preparation enables rapid retrieval and analysis of relevant information, providing accurate answers without excessive processing time
Data Source
AI summary
A self configuring knowledge representation system and method is presented which self configures based on the external stimuli in order to answer the query in a way best suited to the user. The domain knowledge representation here is by way of graphs that allows the knowledge to self configure based on the query intent to give “specific” to the query answers.