Research On Multi-Feature-Based Entity Alignment Method For Knowledge Graphs

Knowledge graph(KG for short)is a semantic network that describes entities,attributes,and their relationships,used for representing and reasoning about structured knowledge.However,knowledge graphs are often constructed from different data sources,and the same entity may have different representations in different knowledge graphs.This heterogeneity can make knowledge sharing and information integration across knowledge graphs very difficult.Additionally,as knowledge graphs continue to grow,the number of entities and relationships becomes increasingly complex,further increasing the difficulty of entity alignment.Currently,problems exist in the entity alignment field such as noise in pre-aligned graphs,ineffective utilization of entity description information,insufficient feature utilization,unscientific fusion methods,and semantic loss caused during information segmentation by BERT models.To address these issues,a multi-feature dynamic adaptive fusion-based entity alignment method is proposed.The main work includes:1.To address issues such as noisy entities and relationships in candidate alignment graphs and lengthy entity description information,a preprocessing method for entity alignment of knowledge graphs for low-entropy information is proposed.This method extracts features from entity names,matches extracted entity name vectors for similarity,and identifies exceptional entities below a certain threshold.The importance score of entity relationships is calculated using the dual graph and Page Rank method,and low-scoring entity relationships are filtered.The TF-IDF technique is used to compress entity description information,alleviating the problem of information loss caused by BERT model segmentation of long texts.Experimental results show that the proposed pre-processing method is effective in filtering noise in the graph and improving alignment accuracy.Furthermore,the compression method effectively compresses information,adapts to BERT models,and improves model processing capabilities.2.This paper proposes an entity alignment method based on multi-feature dynamic adaptive fusion to address the problem of fixed weights commonly used in feature fusion,which results in fused vectors that cannot accurately capture the interaction of various feature information of entities.The method uses BERT models as the basic representation unit to extract features from entity names,descriptions,relationships,and attributes.Additionally,the method extracts visual features using Convolutional Visual-Semantic Embedding(CVSE)model and fuses the visual information of entities.Dynamic adaptive feature fusion is performed based on the information characteristics of each feature,achieving more scientific entity alignment.Finally,experimental results validate the proposed multi-feature dynamic adaptive fusionbased entity alignment method,demonstrating significant improvement in entity alignment accuracy.