Knowledge graph completion-oriented link prediction method

A knowledge map and prediction method technology, applied in the field of computer natural language processing, can solve problems such as triplet error link prediction, and achieve the effect of avoiding error link prediction
CN112883200APending Publication Date: 2021-06-01CHONGQING UNIV
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Patent Information

Authority / Receiving Office
CN Β· China
Patent Type
Applications(China)
Current Assignee / Owner
CHONGQING UNIV
Publication Date
2021-06-01

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Abstract

The invention provides a knowledge graph completion-oriented link prediction method, which comprises the following steps of: sequentially regarding other entities irrelevant to a head entity as tail entities by utilizing a given head entity and a given relationship, and sequentially regarding other entities irrelevant to the tail entities as head entities by utilizing a given tail entity and a given relationship, calculating scores of the triple through a link prediction model, taking the tail entity with the highest score as a predicted tail entity, and taking the head entity with the highest score as a predicted head entity; according to the link prediction model, a global context coding module based on an attention mechanism is introduced into a convolutional neural network, global context information is learned by aggregating local features, and feature representation used for knowledge graph completion is enhanced. The method can solve the technical problem that when a convolutional neural network model carries out knowledge graph embedding learning, due to the fact that context information is not utilized, triads with many complex relations have wrong link prediction.
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Description

technical field

[0001] The invention relates to the technical field of computer natural language processing, in particular to a link prediction method for knowledge graph completion. Background technique

[0002] A knowledge graph is a structured knowledge base for storing some facts, which can be represented as a multi-relational directed graph. The nodes in the knowledge graph represent entities, and each edge represents the relationship between entities (the relationship between entities is hereinafter referred to as relationship). Entities and relationships are represented by triples (s, r, o), where s and o represent the head entity and tail entity respectively, and r represents the relationship between s and o. At present, knowledge graphs have been widely used in many fields of artificial intelligence, such as semantic search, recommendation system, question answering system, information extraction, etc. Although the knowledge graph has millions of triples, most of ...

Claims

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