Knowledge Graph Embedding for Multi-Hop Query Answering
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Solution Overview
Problem
Current knowledge graph-based question answering systems struggle with complex queries involving multiple hops and constraints due to knowledge graph sparsity and inefficiencies in processing and storage, particularly when external text corpora are not readily available.
Innovation Solution
The proposed system employs embedding-based query graph generation, using knowledge graph embeddings to predict missing links and generate query graphs that incorporate constraints, reducing the search space and improving processing speed by transforming natural language queries into vector representations for efficient answer retrieval.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If knowledge graphs are used to store and process information, then information availability is improved, but knowledge graph sparsity causes inadequate handling of complex questions
Solution Approach 1:
The patent introduces an external text corpus as an intermediary resource to supplement the sparse knowledge graph. The system retrieves relevant information from the text corpus when the knowledge graph lacks sufficient data to answer complex questions, thereby bridging the gap between limited structured knowledge and the need for comprehensive information.
Solution Approach 2:
The patent creates a composite information system that combines structured knowledge from the knowledge graph with unstructured text from external corpora. This hybrid approach leverages the strengths of both data sources: the precision of knowledge graphs for factual relationships and the richness of text corpora for contextual information, resulting in a more robust system for answering complex questions.
2Ease of operation
If standard complex question answering systems address each type of complex query separately, then processing simplicity is maintained, but the system is unable to process multiple types of complexities simultaneously
Solution Approach 1:
The patent implements a unified question answering framework that can handle multiple types of complex queries simultaneously. The system uses a single processing architecture that integrates knowledge graph querying, text corpus retrieval, and reasoning capabilities, allowing it to adaptively process constraint-based questions, multi-hop questions, and their combinations without requiring separate specialized systems.
Solution Approach 2:
The patent employs dynamic query generation and processing that adapts to the specific type of complex question encountered. The system dynamically adjusts its retrieval and reasoning strategies based on the question characteristics, switching between knowledge graph-only processing, text corpus augmentation, and hybrid approaches as needed for different query types.
3Quantity of substance
If query graphs are generated without strategic constraint incorporation, then completeness of possible relations is maintained, but search space is large increasing processing time and storage needs
Solution Approach 1:
The patent applies partial constraint incorporation in query graph generation by strategically selecting only the most relevant constraints to apply during different stages of query processing. Rather than applying all possible constraints uniformly, the system selectively applies constraints based on question type and context, reducing the search space sufficiently to improve processing efficiency while maintaining adequate query graph completeness for accurate answer generation.
Data Source
AI summary
Automated response generation systems and methods are disclosed. The systems can include a deep learning model specially configured to apply embedding techniques to redesign natural language querying systems for use over knowledge graphs. The disclosed systems and methods employ knowledge graph embedding to reduce knowledge graph sparsity by performing missing link prediction. The systems and methods described generate query graphs with increased flexibility and the ability to handle multi-hope constrained-based queries.