Graph Data Structure for Natural Language Processing Adaptability
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Solution Overview
Problem
Existing natural language processing techniques are not adapted for continuous or contextualized use across various settings and domains, and struggle to generate responses for input queries without prior knowledge.
Innovation Solution
A system and method using a graph data structure with nodes, links, and evaluation criteria-rating pairs to parse and traverse input, identify problems, and generate solutions by populating a solution graph data structure, allowing for contextual understanding and response generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If semantic networks are used to represent knowledge, then knowledge relationships can be represented, but the system cannot be adapted for continuous or contextualized use in various settings or domains
Solution Approach 1:
The patent transforms the static semantic network into a dynamic graph data structure where nodes and links can be continuously added, removed, and modified during runtime. The graph structure allows for dynamic traversal and population based on incoming natural language inputs, enabling the system to adapt to various domains and settings while maintaining manageable complexity through incremental updates rather than complete system reconfiguration.
Solution Approach 2:
The graph data structure serves multiple functions: it stores knowledge representations, enables contextual understanding through traversal, supports continuous learning by incorporating new information, and generates responses across different domains. This universal structure replaces multiple domain-specific implementations, allowing the same system to handle diverse settings and domains effectively.
2Reliability
If prior knowledge is required for response generation, then accurate responses can be provided for known problems, but the system cannot generate responses for input queries without prior knowledge
Solution Approach 1:
The system performs preliminary population of the graph data structure with available knowledge and evaluation criteria before receiving queries. This pre-population enables the system to have ready-made knowledge frameworks for known problems, ensuring reliable responses. Simultaneously, the graph structure is designed to accommodate new information, allowing the system to adapt to unknown queries by incorporating new nodes and relationships as they arise.
Solution Approach 2:
The system uses evaluation criteria-rating pairs to assess the quality and relevance of generated responses. This feedback mechanism allows the system to learn from both successful and unsuccessful responses, continuously improving its ability to handle both known and unknown queries. The feedback loop enables the graph structure to evolve, incorporating new knowledge patterns that enhance response accuracy for future queries.
3Measurement precision
If extensive contextual situations and domains are captured, then response accuracy improves, but the data structure complexity increases
Solution Approach 1:
The patent segments the knowledge representation into discrete nodes and links within the graph data structure. Each node represents a specific entity or concept, while links represent relationships between them. This segmentation allows the system to capture extensive contextual situations and domains by adding individual nodes and links as needed, rather than managing monolithic complex structures. The modular nature of the graph enables precise responses while keeping data structure complexity manageable through organized, incremental growth.
Data Source
AI summary
According to embodiments described in the specification, systems and methods are provided for natural language processing. A method in a server includes receiving, at the network interface device, an input for response from an electronic device, parsing the input to identify one or more entity data values and one or more relationship data values, populating a graph data structure with the identified entity data values, and the relationship data values, wherein the populating includes applying evaluation criteria-rating pair values, traversing the graph data structure to identify one or more problems indicated by the evaluation criteria-rating pair values, in response to the traversing, determining one or more changes to the graph data structure to satisfy one or more identified problems, if the determination is affirmative, populating a solution graph data structure that satisfies one or more identified problems, and transmitting, to the electronic device, a response to the input.