Large-scale entity alignment method based on bidirectional graph partition and reciprocal reasoning

Abstract

The invention discloses a large-scale entity alignment method based on bidirectional graph partition and reciprocal reasoning. The method comprises the following steps: acquiring data of a source knowledge graph and a target knowledge graph; performing graph partition on the source knowledge graph and the target knowledge graph to obtain a group of sub-graph pairs of source sub-graphs and target sub-graphs; obtaining joint entity representation in all the sub-graph pairs by utilizing a representation learning model of an entity structure; performing reciprocal alignment reasoning on sub-graphs in the sub-graph pairs to obtain alignment results of the sub-graph pairs; and aggregating the alignment results of all the sub-graph pairs to obtain an alignment result of the source knowledge graph and the target knowledge graph. According to the method provided by the invention, through bidirectional partition and aggregation, the final sub-graph pairs can have a more complete structure and more seed entity pairs, so that a more accurate entity alignment result can be generated; and compared with traditional direct alignment reasoning, two-stage reciprocal modeling of reciprocal reasoning can obtain a better alignment effect.

Description

technical field [0001] The present invention relates to the technical field of knowledge graphs, in particular to the technical field of large-scale knowledge graph fusion, and in particular to a large-scale entity alignment method based on bidirectional graph segmentation and reciprocal reasoning. Background technique [0002] As an effective way to organize and store data, knowledge graph has received continuous attention in recent years. Knowledge graphs play an increasingly important role in organizing knowledge and downstream tasks (such as recommender systems, information retrieval, question answering systems, etc.). A knowledge graph is difficult to achieve completeness, because there are always new data and knowledge emerging. In order to increase the scale and coverage of knowledge graphs, a feasible way is to introduce data from other knowledge graphs. And entity alignment plays an important role in this process. Entity alignment aims to find the same real-world...

AI Technical Summary

Problems solved by technology

However, such methods (one-way segmentation methods) are often difficult to meet the first goal, because the segmentation of the second knowledge graph is limited by the constraint of retaining the seed entity pairs, which in turn destroys the knowledge graph to some extent. Structure

Images