Entity Linking Accuracy via Embedding Vectors and Context Semantics
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Solution Overview
Problem
Existing entity linking solutions fail to fully utilize external knowledge information, resulting in low accuracy in entity linking tasks.
Innovation Solution
A method that involves acquiring target text, determining entity mentions and corresponding candidate entities, calculating embedding vectors using a preset model, and incorporating context semantic and type information to improve entity linking accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing entity linking solutions are used, then the process is simple, but the accuracy of entity linking is low due to insufficient utilization of external knowledge information
Solution Approach 1:
The entity linking process is divided into distinct modules: entity mention detection, candidate entity retrieval, embedding vector generation, context semantic analysis, type information extraction, and final linking decision. Each module handles a specific aspect of the task, allowing complex external knowledge to be processed in manageable segments that collectively improve accuracy without overwhelming system complexity
Solution Approach 2:
Embedding vectors are introduced as intermediary representations that bridge the gap between raw entity mentions and the knowledge base. These vectors serve as a mediator that encodes semantic information from multiple sources including external knowledge, enabling more accurate entity linking while maintaining a clear separation between different processing stages
2Measurement precision
If external knowledge information is fully utilized through embedding vectors, context semantic information, and type information, then entity linking accuracy improves, but the computational complexity increases
Solution Approach 1:
Embedding vectors for candidate entities are pre-computed and stored before the actual entity linking task. This preliminary action allows the system to quickly retrieve and utilize rich external knowledge information during entity linking without performing computationally intensive calculations in real-time, thereby improving accuracy while controlling computational resource usage
Solution Approach 2:
The system applies different levels of processing detail to different parts of the input based on their importance. Context semantic information and type information are processed with varying degrees of depth depending on their relevance to the specific entity linking task, allowing the system to focus computational resources on the most critical aspects of improving accuracy
Data Source
AI summary
A method, apparatus, device, and storage medium for linking an entity, relates to the technical fields of knowledge graph and deep learning are provided. The method may include: acquiring a target text; determining at least one entity mention included in the target text and a candidate entity corresponding to each entity mention; determining an embedding vector of each candidate entity based on the each candidate entity and a preset entity embedding vector determination model; determining context semantic information of the target text based on the target text and each embedding vector; determining type information of the at least one entity mention; and determining an entity linking result of the at least one entity mention, based on the each embedding vector, the context semantic information, and each type information.