Entity Linking Accuracy via Descriptive Text Screening
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Solution Overview
Problem
Existing entity linking methods face challenges in achieving high accuracy, particularly in professional fields like biomedicine and chemistry, due to incomplete information in knowledge bases.
Innovation Solution
The proposed entity linking method involves obtaining text content with mention and descriptive text, performing retrieval on preset entity content based on descriptive text, filling screening templates with mention text, merging descriptive text, candidate entity content, and screening templates, and finally screening candidate entity content to determine target entity content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional entity linking methods are used, then the process is simple, but the accuracy is low in professional fields with incomplete knowledge bases
Solution Approach 1:
The entity linking process is divided into multiple stages: initial entity retrieval based on mention text, descriptive text generation for each candidate entity, screening template content creation, and merged text generation for final screening. This segmentation allows each stage to focus on specific aspects, improving overall accuracy while managing complexity through structured processing
Solution Approach 2:
Descriptive text and screening template content are generated in advance for each candidate entity before the final screening stage. This preliminary action prepares comprehensive information that facilitates more accurate entity selection, especially important in professional fields where knowledge bases may be incomplete
2Measurement precision
If knowledge base information is incomplete, then storage requirements are reduced, but entity linking accuracy deteriorates
Solution Approach 1:
Descriptive text and screening template content serve as intermediary elements that bridge the gap between incomplete knowledge base information and accurate entity linking. These intermediaries enrich the information available for decision-making by generating contextual descriptions and structured screening content based on the limited knowledge base data
Solution Approach 2:
The method transforms the information representation by changing from raw knowledge base entries to enriched descriptive text and structured screening templates. This parameter change in information format allows for more nuanced comparison and selection, improving accuracy even when the underlying knowledge base remains incomplete
3Measurement precision
If multiple screening steps are added, then entity linking accuracy is improved, but processing time increases
Solution Approach 1:
Screening template content and descriptive text are generated in advance for all candidate entities, allowing the final screening stage to focus solely on comparison and selection. This preliminary preparation reduces the computational burden during the actual entity linking process, balancing accuracy improvement with reasonable processing time
Data Source
AI summary
An entity linking method is provided. Text content including text characters and descriptive information that explains the text characters is obtained. At least one candidate entity content corresponding to the text characters based on the descriptive information is obtained. First screening template content is filled with content based on the text characters to generate second screening template content. Merged text content is generated based on the descriptive information, the at least one candidate entity content, and the second screening template content. Target entity content corresponding to the text characters is obtained based on the merged text content.