Semantic Parsing Engine for Graph Database Query Translation
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Solution Overview
Problem
Conventional data systems face difficulties in correlating and drawing inferences between disparate data sets due to differing organizational structures and formats, often providing search results with high uncertainty and lacking contextual relations.
Innovation Solution
A semantic parsing system that converts natural language queries into graph queries, utilizing a graph-backed approach to unify datasets and generate connected knowledge graphs, enabling superior search experiences by automatically deriving grammar and semantic concepts from graph schema and incorporating machine learning to establish new relationships.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional data systems organize data into discrete sets with standardized identifiers, then searching within a single entity's data becomes easier and faster, but correlating and drawing inferences between disparate data sets from multiple entities becomes difficult due to differing organizational structures and formats
Solution Approach 1:
The patent introduces a semantic parsing system as an intermediary layer between conventional discrete data sets and the query interface. This system generates semantic graphs that serve as mediators, translating between different organizational structures and formats while preserving the ability to perform fast searches within individual data sets and enabling correlation across disparate data sets through unified semantic representation
Solution Approach 2:
The patent segments the data processing into distinct components: individual data sets maintain their original discrete organization for efficient local searching, while the semantic parsing system creates separate semantic graph representations that enable cross-dataset correlation. This segmentation allows each data set to be optimized independently while the semantic layer provides unified access
2Ease of operation
If conventional data systems retrieve documents from multiple data sets, then data accessibility improves, but the results lack context or relation to other documents leading to unacceptable uncertainty
Solution Approach 1:
The semantic parsing system acts as an intermediary that retrieves documents from multiple data sets while simultaneously generating semantic graphs that capture contextual relationships. These semantic graphs provide the missing context and relations, allowing the system to maintain high data accessibility while improving result certainty through structured semantic information
Solution Approach 2:
The patent creates a composite result structure combining traditional document retrieval with generated semantic graphs. This composite approach integrates the accessibility of conventional search with the contextual richness of semantic relationships, delivering results that are both easily accessible and highly certain
3Measurement precision
If a system uses raw graph query languages to search knowledge graphs, then precise data retrieval is achieved, but the complexity increases making it harder for novice users to navigate and requiring more attempts to complete tasks
Solution Approach 1:
The semantic parsing system serves as an intermediary between simple natural language queries and complex graph query operations. It automatically generates the precise graph queries needed for accurate data retrieval while shielding users from the complexity, maintaining measurement precision while dramatically improving ease of operation
Solution Approach 2:
The system performs self-service by automatically generating semantic graphs and formulating precise queries without requiring user expertise in graph query languages. The semantic parsing component autonomously handles the complexity of translating user intent into precise graph queries, eliminating the need for users to learn complex query syntax
Data Source
AI summary
In various example embodiments, a system and methods are presented for converting query structures for information retrieval from graph-based data structures. The systems and methods receive a natural language query including a set of terms and generate an intermediate semantic relationship of the set of terms of the natural language query. The systems and methods generate a graph query including graph terms corresponding to the set of terms of the natural language query defined by a graph database. The systems and methods search one or more datasets associated with the graph database using the graph query and return a set of results based on the graph query.