Semantic Parsing for Knowledge Base Relation Linking
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Solution Overview
Problem
Current techniques for responding to natural language queries in knowledge bases suffer from poor performance and relevance due to the ambiguity of natural language and the lack of training data for relation extraction and linking.
Innovation Solution
The implementation of a computer-implemented method that translates natural language queries into intermediate representations, converts them into query triples, and performs relation linking with knowledge base triples using semantic parsing and distant supervision, specifically leveraging Abstract Meaning Representation (AMR) and machine learning environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If current techniques are used to respond to natural language queries in knowledge bases, then the system can process queries, but the performance and relevance of results are poor
Solution Approach 1:
The patent introduces an intermediary representation layer between natural language queries and knowledge base triples. The intermediary representation serves as a bridge that captures semantic meaning from natural language and maps it to structured triple format, enabling accurate relation linking without direct complex parsing of KB triples
Solution Approach 2:
The patent changes the representation parameters from raw natural language text to structured intermediary representations with standardized relation types and entity formats. This parameter transformation enables systematic matching with knowledge base triples and improves both relevance and processing efficiency
2Measurement precision
If relation extraction and linking are performed without sufficient training data, then the system can operate with limited data, but the accuracy of relation matching is insufficient
Solution Approach 1:
The patent performs preliminary action by pre-processing natural language queries into intermediary representations before relation linking. This preliminary transformation creates standardized relation templates that can be systematically matched against knowledge base triples, improving accuracy without requiring extensive training data
Solution Approach 2:
The patent creates copies of knowledge base triples in the intermediary representation format, allowing the system to match query relations against standardized triple templates. This copying approach enables accurate relation matching by comparing structured representations rather than raw text, reducing dependency on large training datasets
3Device complexity
If natural language queries are processed directly against knowledge bases, then the system structure remains simple, but lexical gaps and ambiguity reduce query result quality
Solution Approach 1:
The patent introduces an intermediary representation layer that acts as a mediator between natural language queries and knowledge base triples. This intermediary layer standardizes relations and entities, bridging lexical gaps and resolving ambiguities without requiring complex direct parsing of KB triples
Solution Approach 2:
The patent segments the query processing into distinct stages: natural language understanding, intermediary representation generation, and relation linking. This segmentation allows each stage to be optimized independently, maintaining overall system simplicity while improving query result relevance through specialized processing at each step
Data Source
AI summary
A computer-implemented method according to one embodiment includes identifying a natural language query; translating the natural language query into an intermediate representation; converting the intermediate representation into one or more query triples; and performing relation linking between each of the one or more query triples and a plurality of knowledge base triples.