Information Extraction Using Knowledge Base Alignment
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Solution Overview
Problem
Traditional relation extraction methods require extensive annotated data, which is time-consuming and costly to obtain, and often result in noisy training corpora due to inconsistent semantic relations.
Innovation Solution
The method automatically gathers labeled data by aligning entities in text with those in a knowledge base, using nonnegative matrix factorization to identify semantically consistent instances and reduce noise, thereby improving efficiency and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional relation extraction methods use extensive annotated data for training, then the model can learn semantic relationships, but the annotation process becomes time-consuming and costly
Solution Approach 1:
The patent performs preliminary actions by automatically gathering initial labeled data through entity alignment with knowledge bases before formal model training. This pre-processing step creates a foundation of training data without manual annotation, reducing the time and cost burden while maintaining sufficient quality for effective relation extraction learning
Solution Approach 2:
The system serves itself by automatically generating training data through alignment with existing knowledge bases. The model uses readily available knowledge base data to create its own training corpus, eliminating the need for external manual annotation services and reducing dependency on time-consuming human labeling processes
2Quantity of substance
If traditional methods collect all sentences containing entity pairs, then data coverage is comprehensive, but noisy instances with inconsistent semantic relations reduce extraction accuracy
Solution Approach 1:
The patent extracts only the useful and consistent portions of training data by filtering sentences based on semantic consistency checks. Instead of using all sentences containing entity pairs, the method selectively extracts instances that align with knowledge base relations, removing noisy data that would degrade model performance
Solution Approach 2:
The system changes the quality parameter of training data by applying semantic consistency filtering. It transforms raw, potentially noisy sentence data into high-quality training instances by verifying alignment with knowledge base relations, thereby improving the reliability of the training corpus without significantly reducing its volume
3Reliability
If manual annotation is used to ensure data quality, then semantic consistency is maintained, but the process becomes costly and slow
Solution Approach 1:
The system performs self-verification by automatically checking semantic consistency through alignment with knowledge bases. This self-service mechanism replaces manual quality checking, maintaining high data reliability while dramatically improving productivity by eliminating the need for time-consuming human review of each training instance
Solution Approach 2:
The patent introduces knowledge bases as an intermediary to verify semantic consistency. Instead of relying on manual annotation for quality control, the system uses structured knowledge bases as a mediator to automatically validate whether extracted sentences maintain consistent semantic relationships, ensuring data quality at scale
Data Source
AI summary
The present invention is related to an information extraction method. The information extraction method may comprise providing r semantic relationships, acquiring entity pairs corresponding to the semantic relationships, acquiring first instances based on the entity pairs, and eliminating instances that do not have the semantic relationships from the first instances to obtain second instances. r is a positive integer. Each of the entity pairs contains a pair of named entities. The first instances are sentences containing the entity pairs.