Research And Implementation Of Entity Alignment Algorithm Based On Knowledge Representation Learning

With the rapid development of the information age,the total amount of network data has also shown explosive growth.It is estimated that the total amount of network data in 2022 has reached a scale of 61 ZB.In order to effectively process and utilize these data,researchers have proposed a knowledge graph that describes concepts,entities,and their relationships in the objective world in a structured form.Knowledge graph can express information on the Internet in a form that is easy for humans to understand,assisting them in better utilizing this information.However,using a single knowledge graph as an information source still faces issues such as missing information and errors.In response to this issue,researchers have proposed a solution for knowledge graph fusion,which improves the quality of knowledge graphs by integrating different knowledge graphs.Entity alignment is the most fundamental and critical technology in knowledge graph fusion,aiming to find entities in different knowledge graphs that correspond to the same real thing,in order to assist in the fusion of other information within the knowledge graph.In the past,the common entity alignment model based on knowledge representation learning is trained based on relational triplets and pre aligned seed pairs.The entity structure vector representation is projected into a unified vector space,and then the similarity between corresponding entities is measured by calculating the distance between vectors to achieve entity alignment.This type of method requires a large amount of manual screening of pre aligned seed pairs and fails to utilize the attribute triplets of each entity in the knowledge graph.In response to the above issues,this article conducted in-depth research on entity alignment methods,and the main work includes the following three parts:(1)In response to the problem that entity alignment methods based on knowledge representation learning cannot utilize attribute information,this paper proposes an entity alignment algorithm based on Sim CSE and Trans E.This algorithm combines knowledge representation learning models and pre trained language models for entity alignment tasks,generates vector representations of entity structures through Trans E models,and generates vector representations of entity attributes through Sim CSE models,Then combine the two vector representations for entity alignment.This way,the information within the knowledge graph can be more fully utilized,which can improve the entity alignment effect.Through experiments,it has been proven that this algorithm can effectively improve the accuracy of entity alignment.(2)Aiming at the problem that the cost of large-scale manual screening of seed entity pairs is too high,this paper proposes an iterative entity alignment algorithm based on semi supervised learning.The algorithm relies on a small number of manually aligned seed entity pairs to perform iterative entity alignment,and based on the two-way alignment strategy and curriculum learning strategy,it selects aligned entities whose distance between vectors is less than the threshold as new seed entity pairs,so as to gradually expand the size of seed entity pairs,This effectively improves the effectiveness of entity alignment while reducing the labor cost of entity alignment.Through experiments,it has been proven that this algorithm can improve entity alignment while reducing the size of manually selected seed entity pairs.(3)Based on the entity alignment algorithm and iterative entity alignment algorithm proposed above in this paper,this paper constructs an entity alignment network system platform for open source knowledge mapping.This paper uses the idea of microservice to design the overall architecture of the entire system,which ensures the high availability and low coupling of the system.This system supports users to upload knowledge graph data and set parameters for entity alignment tasks according to their needs,thereby helping users better conduct various knowledge graph research.After practical operation verification,the system can stably achieve the various functions designed in this article.