A Knowledge Graph Fusion Method Based on Entity Alignment

Abstract

The invention discloses a knowledge map fusion method based on entity alignment, comprising the following steps: acquiring data of two knowledge maps; learning the structure vector of the entity by using a graph convolution network, and expressing the name of the entity as a word vector; calculating the data between the entities The comprehensive distance is used to represent the similarity between entities; an iterative training framework based on curriculum learning is used for entity recognition and alignment; according to the results of entity alignment, two knowledge maps are merged into one knowledge map. The method of the invention designs a basic framework of entity alignment that integrates structural features and entity name features; designs an iterative training method based on curriculum learning, expands training data from easy to difficult, and uses a word-shift distance model to repeat the pre-order alignment results. Sorting to fully mine entity name information, making the integration of knowledge graphs more accurate and comprehensive.

Description

technical field [0001] The invention belongs to the field of knowledge map generation and fusion, in particular to a knowledge map fusion method based on entity alignment. Background technique [0002] In recent years, a large number of knowledge graphs (KG) have emerged, such as YAGO, DBpedia, NELL, and Chinese CN-DBpedia, Zhishi.me, etc. These large-scale knowledge graphs play an important role in intelligent services such as question answering systems and personalized recommendations. In addition, in order to meet the needs of specific fields, more and more domain knowledge graphs are derived, such as medical knowledge graphs. In the process of knowledge graph construction, it is inevitable to make a trade-off between coverage and accuracy. And any knowledge graph cannot be complete or completely correct. [0003] In order to improve the coverage and accuracy of knowledge graphs, a feasible method is to introduce relevant knowledge from other knowledge graphs, because ...

AI Technical Summary

Problems solved by technology

To solve this problem, some methods propose to use iterative training (IT) to select high-confidence entity pairs from the test set results for the next round of training, but there are problems such as easy introduction of error samples and low efficiency.

In addition, on datasets with real-world distributions, these iterative training frameworks can only introduce a small number of high-confidence entity pairs, which cannot bring significant performance improvements.

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