Knowledge Graph Query Builder for Automated Data Retrieval
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Solution Overview
Problem
Large databases with diverse and complex data structures pose challenges in generating accurate and efficient data retrieval queries, leading to inconsistencies and errors due to the complexity of manually formulating queries that join and filter data from multiple data sets.
Innovation Solution
A method and system that utilize a semantic model stored on a separate data store, structured as a knowledge graph, to automatically generate data retrieval queries by receiving user inputs, retrieving metadata, and generating queries in a format optimized for querying, thereby simplifying the process and improving accuracy and speed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual query formulation is used to retrieve data from large databases, then data retrieval accuracy can be maintained, but the complexity and time required to formulate queries increases significantly
Solution Approach 1:
The patent introduces a knowledge graph as an intermediary layer between the user and the database. The knowledge graph stores semantic relationships and metadata about the data, allowing users to query using natural language or simplified syntax rather than complex query languages. This intermediary translates high-level user intent into precise database queries, maintaining retrieval accuracy while reducing formulation complexity.
Solution Approach 2:
The patent replaces the mechanical process of manual query construction with an automated semantic interpretation system. Instead of requiring users to manually construct queries according to database schema and query language syntax, the system automatically interprets user intent through semantic analysis and generates appropriate queries, substituting the manual mechanical process with an intelligent automated system.
2Measurement precision
If manual query formulation is used to join and filter data from multiple data sets, then query accuracy can be maintained, but the time required for query generation increases
Solution Approach 1:
The patent performs preliminary action by pre-processing and storing semantic relationships, data relationships, and metadata in the knowledge graph before query execution. This pre-computed semantic structure allows the system to quickly understand data relationships and generate accurate queries without requiring users to manually analyze and construct complex join and filter logic during query formulation time.
Solution Approach 2:
The patent substitutes the manual mechanical process of analyzing multiple data sets and constructing join/filter logic with an automated semantic interpretation system that leverages pre-computed relationships in the knowledge graph to rapidly generate accurate queries.
3Manufacturing precision
If detailed knowledge of data structure and query languages is required, then query precision can be improved, but the ease of operation decreases
Solution Approach 1:
The knowledge graph serves as an intermediary that shields users from the complexity of underlying data structures and query languages. Users interact with the system through natural language or simplified interfaces, while the knowledge graph handles the translation to precise database queries, eliminating the need for users to have detailed knowledge of data schemas or query language syntax.
Solution Approach 2:
The patent replaces the requirement for users to possess detailed knowledge of data structures and query languages with an automated semantic interpretation system that handles the complexity internally, allowing users to formulate queries without specialized knowledge while maintaining high precision.
4Quantity of substance
If complex data structures with diverse information types are stored, then data comprehensiveness is improved, but the difficulty of reviewing and understanding data increases
Solution Approach 1:
The knowledge graph acts as an intermediary that organizes and presents complex data structures in a semantically meaningful way. It provides a unified view of diverse information types by modeling their relationships and contexts, making it easier to review and understand comprehensive data without being overwhelmed by structural complexity.
Solution Approach 2:
The patent applies local quality by organizing diverse data types with their specific characteristics and relationships in localized semantic contexts within the knowledge graph. Each data type and relationship is modeled with appropriate semantic properties, allowing users to understand and review data in contextually relevant ways rather than as a monolithic complex structure.
Data Source
AI summary
The present disclosure concerns automatically generating data retrieval queries. A system may include data residing on a first data store and a knowledge graph residing on a second data store. The knowledge graph may include a semantic model of the data. The knowledge graph may be structured differently than the data and may be stored in a format different from the data. The system may include a query builder that receives one or more inputs through a user interface. The user interface may be based in part on the knowledge graph. The one or more inputs may indicate a subset of the data. The query builder may retrieve metadata from the knowledge graph based on the one or more inputs. The query builder may then generate a query for retrieving the subset of the data using the one or more inputs and the metadata retrieved from the knowledge graph.