A Method and Device for Entity Alignment Based on Edge Type Attention Mechanism

Abstract

The invention discloses an entity alignment method based on an edge type attention mechanism, including constructing a type dual graph of an electric power knowledge map; using an attribute attention mechanism to train node features of a type dual graph and obtaining the embedding of each node in the type dual graph ; Convert the nodes in the type dual graph to the attention coefficient of the relationship in the knowledge graph, and gather the neighbor entity and relationship information according to the relationship attention coefficient of the nodes in the knowledge graph, and get the final structured embedding result; calculate the loss function and align entities in different knowledge graphs to obtain entity pairs aligned with entities in different knowledge graphs. The invention also discloses a device for realizing the entity alignment method based on the edge type attention mechanism. The invention broadens the thought of relational information mining, excavates the structural features of entities, and has high precision, good practicability, simplicity and reliability.

Description

technical field [0001] The invention belongs to the field of power system big data processing, and in particular relates to an entity alignment method and device based on an edge type attention mechanism. Background technique [0002] In the task of building a large-scale knowledge base, it is necessary to process a large amount of entity data from multi-source knowledge bases, which are usually stored in the knowledge base in the form of knowledge graphs, such as figure 1 shown. Therefore, at the beginning of building a knowledge base, it is first necessary to establish a knowledge fusion method to fuse multi-source knowledge graphs. Due to the different sources of information in different knowledge bases, as well as the differences in manual definition and proofreading, semantically identical entities will have different representations in different knowledge bases. Entities with the same name may represent semantically the same thing, or two things. Therefore, before k...

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Problems solved by technology

However, previous methods treat neighbor nodes equally, so they cannot perceive the weight of the relationship, nor can they guarantee the consistency of the same type of relationship in the training process.

The second is that the relationship attributes have not been fully utilized, and the aligned nodes are also aligned with the surrounding relationships. Effective use of relationship attribute information can obtain better entity embedding, such as in figure 2 Among them, the relationship type, unit, and material connected to the Chinese entity resistor, and the relationship type (type), unit (unit), and material (material) connected to the English entity resistance are aligned

[0005] In summary, current KG entity alignment methods ignore the role of relation type information in entity alignment

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