Knowledge Graph Query Templates from Question-Answer Pairs
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The generation of query templates for knowledge-graph based question answering (KGQA) systems is laborious and time-consuming, relying heavily on manual labor and extensive training data, which hinders the efficiency of building these systems.
Innovation Solution
A method and system that automatically generates candidate query templates using the knowledge graph schema and filters them using a real question-answer corpus, reducing the need for manual development and accelerating the process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual methods are used to generate query templates, then the quality and accuracy of query templates can be ensured, but the time and labor required increase significantly
Solution Approach 1:
The system performs self-service by automatically generating query templates through machine learning models that learn from existing question-answer pairs and knowledge graph schemas, eliminating the need for manual template creation while maintaining high accuracy through iterative learning and validation processes
Solution Approach 2:
The system performs preliminary action by pre-processing question-answer pairs to extract entities, relationships, and patterns before template generation, and by pre-defining knowledge graph schemas that guide the template synthesis process, thereby reducing the time required for actual template creation
2Reliability
If extensive training data is used to build KGQA systems, then the system performance and accuracy improve, but the complexity and resource requirements increase
Solution Approach 1:
The system extracts only the essential elements needed for query template generation from large corpora, including entity-relationship patterns, question templates, and knowledge graph schemas, rather than processing entire datasets, thereby reducing complexity while maintaining performance
Solution Approach 2:
The system segments the template generation process into distinct modules: question parsing, entity extraction, relationship identification, template selection, and validation. Each module handles specific tasks independently, reducing overall system complexity while achieving high performance through coordinated operation of specialized components
3Productivity
If automated methods are used to generate query templates, then the building speed of KGQA systems increases, but the precision and quality control become more challenging
Solution Approach 1:
The system implements feedback mechanisms where generated query templates are validated against the knowledge graph schema and tested with sample questions. Templates that fail validation or produce incorrect results are refined or rejected, ensuring quality control while maintaining automated generation speed through iterative improvement cycles
Solution Approach 2:
The system employs dynamic template generation that adapts to different knowledge graph schemas, question types, and domain-specific requirements. The generation process adjusts its parameters and strategies based on the input characteristics, maintaining high quality across diverse scenarios while preserving automation benefits
Data Source
AI summary
A processor obtains a pair including a question and an answer in natural language; determines at least one entity in the question and an entity type of each of the at least one entity consistent with the schema of a knowledge graph (KG); identifies a subset of candidate query templates based on the entity type of each of the at least one entity, wherein the candidate query templates are generated based on the schema of the KG; composes a set of queries by populating the at least one entity into each of the subset of candidate query templates; executes the set of queries on the KG to generate respective answers; identifies a first answer from the respective answers that is matching with the answer in the pair; and determines a candidate query template, from the subset of candidate query templates, corresponding to the first answer as a query template.