Research And Implementation Of Graph-based Entity Linking Algorithm

With the rapid development of the internet and mobile internet,massive amounts of data are generated daily,most of which are unstructured.Enabling computers to understand such unstructured data greatly benefits natural language processing and other artificial intelligence fields.Entity linking technology connects entity mentions identified in unstructured text with corresponding entities in structured knowledge graphs,eliminating ambiguity and significantly improving the computer’s ability to understand text.In recent years,thanks to the advancements in graph neural networks,numerous graph neural network models have emerged.Knowledge graphs naturally exhibit graph structures that enable better mining of relationships between entity mentions,leading to the rapid development of graph-based entity linking algorithms.In this study,we conducted an in-depth investigation of the research related to entity linking and graph neural networks.Combining existing entity linking methods from both domestic and international sources,we propose two graph-based entity linking models: a multistructure graph fusion entity linking algorithm and a multi-topic consistency global entity linking model.These models aim to further enhance the accuracy and efficiency of entity linking,helping computers process and understand unstructured data and promoting the development of natural language processing and other artificial intelligence fields.In the multi-structure graph fusion entity linking algorithm,we designed three graph structures: the entity mention semantic association graph,the entity mention hierarchical relationship graph,and the candidate entity type association graph.The entity mention semantic association graph interconnects entity mentions and uses a graph attention network to effectively represent entity mentions.We innovatively proposed a method for constructing a hierarchical relationship graph.By establishing an entity mention hierarchical relationship graph and modeling it using a graph-directed network,we obtained an entity mention representation containing hierarchical relationships,greatly enriching the semantic information of entity mention representations.Additionally,based on the type information of candidate entities,we constructed a candidate entity type association graph,which is a heterogeneous graph.Using a graph convolutional neural network to learn the representations of candidate entities,we obtained ranking scores for candidate entities through these three graph structures.This method comprehensively considers various aspects of entity mentions,such as semantic associations,hierarchical relationships,and candidate entity types,thereby improving the performance of the entity linking algorithm.To address the limitations of using topic consistency features in current entity linking tasks,we propose a multi-topic global consistency entity linking model.We believe that multiple topics may exist in long texts,and therefore we introduce a graph-based multitopic extraction method.In addition,we combine graph structures and graph gated neural networks to design an innovative graph neural network approach and a novel graph readout technique.By employing these methods,we successfully extract multi-topic consistency features.Comparing our model with various related models across multiple datasets,we find that our model outperforms others in terms of performance,thereby validating our hypothesis.Moreover,we discover that associatively modeling entity mentions and candidate entities in the construction of the entity linking model yields better results than separate modeling,addressing the shortcomings of our first model.