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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

Pending Publication Date: 2021-06-01
CHONGQING UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

This method can solve the technical problem that the convolutional neural network model in the prior art does not use context information when performing knowledge graph embedding learning, resulting in wrong link predictions for many complex relationship triples

Method used

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  • Knowledge graph completion-oriented link prediction method
  • Knowledge graph completion-oriented link prediction method
  • Knowledge graph completion-oriented link prediction method

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Embodiment 1

[0046] This embodiment provides a link prediction method for knowledge graph completion, which uses a link prediction model to complete the knowledge graph. Such as figure 1 As shown, the construction process of the link prediction model is as follows:

[0047] S1. Construct invalid triples according to valid triples in the existing knowledge graph;

[0048] In the existing knowledge graph, there are a certain number of valid triples, and the invalid triples for convolutional neural network training are constructed based on the valid triples. The method of construction is as follows:

[0049] Use the Bernoulli strategy to generate invalid triples from the valid triple set, specifically: for all triples containing the relationship r, use tph to represent the average number of tail entities corresponding to each head entity; use hpt to represent the average The number of head entities corresponding to each tail entity; the parameters of the Bernoulli distribution are taken as...

Embodiment 2

[0082] An electronic device is provided, comprising:

[0083] one or more processors;

[0084] storage means for storing one or more programs;

[0085] When one or more programs are executed by one or more processors, the one or more processors implement the link prediction method for knowledge graph completion provided by Embodiment 1.

[0086] In a specific implementation, the link prediction model is written in python, the framework is TensorFlow, and the GPU model is 1080TI.

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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.

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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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/36G06N3/04G06N3/08
CPCG06F16/367G06N3/08G06N3/047G06N3/048G06N3/045
Inventor 钟将朱伟王臣戴启祝余尧
Owner CHONGQING UNIV
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