Query Semantic Graphs for Natural Language Question Answering
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Solution Overview
Problem
Current question answering systems face significant computational challenges in disambiguating natural language questions due to ambiguity, leading to inefficient processing and potential inaccuracies when translating them into SPARQL queries for RDF repositories.
Innovation Solution
The method converts natural language questions into query semantic graphs, which are then searched within RDF graphs to find matching subgraphs, thereby eliminating the need for explicit disambiguation during the understanding phase and completing disambiguation during the subgraph matching process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If natural language questions are translated into SPARQL queries for RDF repositories, then the system can answer questions using structured data, but the disambiguation process becomes computationally expensive and inefficient
Solution Approach 1:
The patent performs preliminary action by converting natural language questions into query semantic graphs before the actual query execution. This preliminary conversion captures the semantic structure and relationships in advance, allowing the system to efficiently match against RDF graphs without performing expensive disambiguation during query execution. The semantic graph serves as a pre-processed intermediate representation that preserves meaning while enabling faster processing.
Solution Approach 2:
The patent introduces query semantic graphs as an intermediary between natural language questions and SPARQL queries. This intermediary structure captures the semantic relationships and constraints of the original question in a form that is easier to process against RDF data. The semantic graph acts as a mediator that translates the ambiguity of natural language into structured semantic relationships, reducing the computational burden of disambiguation while maintaining answer accuracy.
2Measurement precision
If explicit disambiguation is performed during the question understanding phase, then potential inaccuracies are reduced, but computational costs increase significantly
Solution Approach 1:
The patent performs preliminary semantic analysis by constructing query semantic graphs that capture the essential meaning and relationships of the question. This preliminary action identifies key entities, predicates, and their relationships without requiring exhaustive disambiguation of all possible interpretations. The semantic graph structure preserves the core meaning while filtering out unnecessary ambiguities, achieving accurate interpretation with reduced computational cost.
Solution Approach 2:
The patent extracts only the essential semantic information needed for answering the question, rather than performing complete disambiguation of all possible meanings. The query semantic graph extracts key entities, relationships, and constraints from the natural language question, discarding redundant or irrelevant ambiguities. This selective extraction maintains interpretation accuracy while significantly reducing the computational resources required compared to exhaustive disambiguation approaches.
Data Source
AI summary
Natural language question answering method and apparatus in the field of information retrieval and processing are provided. The method includes: acquiring a natural language question N; converting the natural language question N into a query semantic graph Qs, each edge in the query semantic graph Qs representing one semantic relation in the natural language question N; searching an RDF graph G for a subgraph matching the query semantic graph Qs; and obtaining an answer to the natural language question N according to the subgraph.