Knowledge graph long-tail relation completion method based on attention mechanism

Abstract

The invention belongs to the field of knowledge maps, particularly relates to a knowledge map long-tail relation completion method, system and equipment based on an attention mechanism, and aims at solving the problem that a traditional relation completion model generates overfitting on long-tail relation prediction due to the fact that the number of long-tail relations is small. The method of thesystem comprises the steps of obtaining a knowledge graph to be completed, constructing the knowledge graph to be completed into a first knowledge graph and a second knowledge graph according to a relationship type between entities of the knowledge graph, obtaining entity vector representation fusing neighborhood information in the first knowledge graph to serve as first representation, performing vectorization representation on each entity in the second knowledge graph according to the first representation, constructing a support set and a query set, obtaining the relationship type label ofeach entity pair in the query set through a preset long-tail relationship prediction method of multiple network types, and complementing the relationship between the entities. According to the method,the neighborhood information of each entity in the knowledge graph is fused, so that the problem of overfitting during long-tail relation prediction is avoided.

Description

technical field [0001] The invention belongs to the field of knowledge graphs, and in particular relates to a method, system, and device for completing long-tail relations in knowledge graphs based on an attention mechanism. Background technique [0002] Knowledge graphs are key resources for artificial intelligence and natural language processing, and play an important role in the fields of question answering systems, intelligent retrieval, and sentiment analysis. Therefore, to complete incomplete knowledge graphs, that is, to complement missing relationships in knowledge graphs Completeness is the basic task of knowledge graphs, and the quality of knowledge completion directly affects the performance of knowledge graphs in downstream tasks. [0003] The translation-based embedding model is the current knowledge graph completion method. This type of method is based on the distributed vector representation of entities and relationships, and the relationship in each triple in...

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Problems solved by technology

[0004] In order to solve the above-mentioned problems in the prior art, that is, in order to solve the problem of over-fitting the long-tail relationship prediction in the traditional relationship completion model due to the scarcity of long-tail relationship numbers, the first aspect of the present invention proposes an attention-based The knowledge map long-tail relationship completion method of the force mechanism, the method includes:

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