Query Refinement Tree for SPARQL Debugging
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Solution Overview
Problem
The lack of effective tools and debugging support for refining SPARQL queries in RDF-based knowledge graphs leads to queries that do not provide expected results, with users facing difficulties in understanding and improving their queries due to the complexity of the SPARQL standard and limited tool support.
Innovation Solution
A system and method that utilize a search engine to navigate a tree of query modification operations, applying heuristic scoring to select and apply query modification operators to refine queries based on differences between expected and current results, guided by a machine learning model that learns from user interactions and query results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If SPARQL query language is used for RDF-based knowledge graphs, then query capability is improved, but tool support and debugging capability deteriorate
Solution Approach 1:
The patent introduces an intermediary system that mediates between the user and the complex SPARQL query language. This system provides a graphical user interface and automated debugging tools that translate user-friendly operations into SPARQL queries, thereby maintaining query capability while improving ease of operation through enhanced tool support
Solution Approach 2:
The patent implements self-service mechanisms through automated debugging and query refinement systems. The system automatically detects query errors, suggests corrections, and optimizes query performance without requiring deep user expertise in SPARQL, thus improving tool support while preserving the power of SPARQL querying
2Loss of information
If SPARQL standard EXPLAIN functionality is used, then query execution plan information is provided, but understanding and debugging complexity increases
Solution Approach 1:
The patent introduces an intermediary layer that translates the technical query execution plan information into user-friendly visual representations and explanations. This intermediary system maintains access to detailed execution plan information while presenting simplified, easily understandable debugging information to users, thereby reducing debugging complexity
Solution Approach 2:
The patent replaces the mechanical interpretation of raw execution plan data with intelligent analysis and visualization systems. Instead of requiring users to manually parse technical execution plans, the system automatically analyzes and presents actionable debugging information, reducing the complexity burden on users
3Extent of automation
If machine learning is used to produce semantic queries from examples, then query construction is automated, but user guidance and query improvement capability are lost
Solution Approach 1:
The patent implements feedback mechanisms where the system provides users with explanations for automated query construction decisions and allows iterative refinement. Users receive feedback on why certain queries are generated and can guide the system to improve queries, maintaining automation while restoring user guidance capability
Solution Approach 2:
The patent creates a dynamic system where the balance between automation and user guidance can adjust based on user needs and expertise level. The system can operate in highly automated mode for simple tasks and transition to more interactive guidance mode for complex queries, providing flexibility in the automation-guidance trade-off
Data Source
AI summary
The disclosed relates to a system for generating a refined query, whereby the system comprises or is coupled with a search engine for searching through a tree of query modification operations, whereby the root node of said tree is an empty node which represents a given initial query, and comprises at least one processor which is configured to perform the following steps:a) defining a set of query modification operators which can be inserted into said tree;b) receiving a second set of reference query results;c) receiving a first set of current query results from a currently given query comprising one or more triple patterns;d) contrasting the first set of query results with the second set of query results by assessing the differences between the two query results;e) running the search engine which is configured to perform the following steps:f) selecting a node of said tree by a computed score derived from the assessed result;g) selecting any query modification operator of the defined set of query modification operators;h) if the selected query modification operator does not correspond to any of the triple patterns of the query represented by the selected node, then continue step f);i) otherwise identifying at least one triple pattern of the query which the selected query modification operator corresponds to;j) generating a refined query by applying the selected query modification operator to the identified triple pattern.