Two-Stage Neural Knowledge Graph Question Answering
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Solution Overview
Problem
Conventional knowledge graph question answering (KGQA) systems face challenges in achieving robustness and accuracy due to the limitations of pipeline-based approaches, which require significant time and expertise to refine, and NMT-based approaches struggle with unseen entities/relationships and large vocabulary sizes.
Innovation Solution
A two-stage neural approach using neural machine translation (NMT) and a neural graph search module, combined with a noise simulator to handle complex questions and mask entities/relations, enabling the generation of SPARQL queries that can handle unseen entities/relationships and reduce vocabulary size.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If pipeline-based approaches are used for KGQA systems, then accuracy can be improved through refined processing steps, but device complexity and time consumption increase significantly
Solution Approach 1:
The patent divides the query generation process into two distinct stages: a NMT-based Seq2Seq model that translates questions to SPARQL silhouettes, and a neural graph search module that predicts correct relations. This segmentation allows each component to specialize in specific tasks, improving overall accuracy while maintaining manageable complexity through modular architecture.
Solution Approach 2:
The patent introduces SPARQL silhouettes as an intermediary representation between natural language questions and final SPARQL queries. This intermediate form captures the essential structure and entities of the query without requiring complete relation prediction, thereby simplifying the translation process and improving accuracy through progressive refinement.
2Ease of operation
If NMT-based approaches are used for query translation, then processing speed and ease of operation improve, but reliability decreases when handling unseen entities and relationships
Solution Approach 1:
The patent performs preliminary entity and relation extraction from the input question before the NMT translation process. This preliminary action ensures that known entities and relationships are identified and handled correctly, providing a solid foundation for the NMT model to work with, thereby improving reliability without sacrificing the ease of operation of the overall system.
Solution Approach 2:
The patent implements a feedback mechanism where the neural graph search module refines the SPARQL silhouette generated by the NMT model by predicting and correcting relation predictions. This feedback loop allows the system to learn from its own outputs and improve reliability for unseen entities and relationships while maintaining the operational simplicity of the NMT approach.
3Adaptability or versatility
If large vocabulary sizes are used in NMT models, then coverage of entities and relationships improves, but loss of time and computational resources increases
Solution Approach 1:
The patent applies local quality by using entity and relation type information locally within the SPARQL silhouette structure. Instead of requiring the NMT model to learn and output every possible entity and relationship from a large vocabulary, the system focuses on extracting and representing only the locally relevant entities and relationships needed for each specific query, thereby reducing time consumption while maintaining comprehensive coverage.
Data Source
AI summary
A modular two-stage neural architecture is used in translating a natural language question into a logic form such as a SPARQL Protocol and RDF Query Language (SPARQL) query. In a first stage, a neural machine translation (NMT)-based sequence-to-sequence (Seq2Seq) model translates a question into a sketch of the desired SPARQL query called a SPARQL silhouette. In a second stage a neural graph search module predicts the correct relations in the underlying knowledge graph.