Knowledge Graph Query Response for Accurate Enterprise NLP
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Solution Overview
Problem
Conventional communication management approaches in enterprises face challenges with natural language understanding and generalizing complex queries, leading to errors and resource-intensive delays due to geographic variability and market dynamics.
Innovation Solution
A computer-implemented method using knowledge graphs and artificial intelligence techniques to generate context-based responses to natural language queries, incorporating enterprise-specific data and ontologies to provide accurate and contextual answers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional communication management approaches are used, then implementation is simpler, but natural language understanding accuracy deteriorates
Solution Approach 1:
The system segments the natural language processing task into multiple specialized components: a knowledge graph processor for structured enterprise data, an AI model for semantic understanding, and a result integration module. Each component handles specific aspects of the query, improving overall accuracy while managing complexity through division of labor.
Solution Approach 2:
The patent introduces an intermediary knowledge graph that bridges structured enterprise data and unstructured natural language queries. This intermediary layer translates between different data representations, enabling accurate understanding without requiring the entire system to be overly complex.
2Productivity
If conventional approaches are used, then resource consumption is lower, but processing speed deteriorates
Solution Approach 1:
The system employs partial action by selectively querying only relevant portions of the knowledge graph and AI model based on the specific natural language query. Instead of processing all available data uniformly, the system identifies and processes only the necessary subset, improving speed while controlling resource consumption.
Solution Approach 2:
The knowledge graph is pre-processed and structured in advance, with enterprise data organized into ready-to-query relationships. This preliminary organization enables faster query processing without requiring excessive computational resources during actual query execution.
3Adaptability or versatility
If conventional communication management is used, then system complexity is lower, but adaptability to market dynamics deteriorates
Solution Approach 1:
The system achieves universality by designing a multi-functional architecture where the knowledge graph and AI model can handle various types of natural language queries across different enterprise domains. The same core infrastructure adapts to diverse market dynamics and enterprise-specific needs without requiring separate specialized systems.
Solution Approach 2:
The system incorporates dynamic elements through the AI model's ability to learn and adapt from new data, and the knowledge graph's capacity to be updated with changing enterprise information. This dynamic nature enables the system to respond to evolving market conditions while maintaining a manageable architectural framework.
Data Source
AI summary
Methods, apparatus, and processor-readable storage media for automatically generating context-based responses to natural language queries using knowledge graphs are provided herein. An example computer-implemented method includes generating at least one query in a predetermined query language by processing at least one natural language query of at least one user of an enterprise; generating a first set of results by processing the at least one query in the predetermined query language using one or more data sources including at least one enterprise-related knowledge graph; generating a second set of results by processing the at least one natural language query using one or more artificial intelligence techniques; generating a third set of results by incorporating at least a portion of the first set into at least a portion of the second set; and performing automated actions based on the third set of results.