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Knowledge graph completion method based on path awareness of convolutional network and related equipment

A knowledge map and convolutional network technology, applied in the field of data mining and deep learning, can solve problems affecting the accuracy of knowledge maps and achieve the effect of improving accuracy

Pending Publication Date: 2022-04-08
BEIJING UNIV OF POSTS & TELECOMM
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the existing neural network-based knowledge map completion methods usually only consider the entity and relationship information of triples in the knowledge map, and ignore the path information that contains rich reasoning modes in the knowledge map, which seriously affects the knowledge map completion. accuracy rate

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  • Knowledge graph completion method based on path awareness of convolutional network and related equipment
  • Knowledge graph completion method based on path awareness of convolutional network and related equipment
  • Knowledge graph completion method based on path awareness of convolutional network and related equipment

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Embodiment Construction

[0027] In order to make the purpose, technical solutions and advantages of the present disclosure clearer, the present disclosure will be further described in detail below in conjunction with specific embodiments and with reference to the accompanying drawings.

[0028] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the embodiments of the present disclosure shall have ordinary meanings understood by those skilled in the art to which the present disclosure belongs. "First", "second" and similar words used in the embodiments of the present disclosure do not indicate any sequence, quantity or importance, but are only used to distinguish different components. "Comprising" or "comprising" and similar words mean that the elements or items appearing before the word include the elements or items listed after the word and their equivalents, without excluding other elements or items. Words such as "connected" or "connected" are not lim...

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Abstract

The invention provides a path-aware knowledge graph completion method based on a convolutional network and related equipment. The method comprises the following steps: acquiring an initial triple set from a knowledge graph; determining a candidate triple set based on the initial triple set; inputting the candidate triple set into a pre-constructed sorting model, outputting a relation score of each candidate triple in the candidate triple set through the sorting model, determining at least one candidate triple from the candidate triple set based on the relation scores, and supplementing the at least one candidate triple into the knowledge graph; wherein the sorting model is pre-trained. According to the completion method disclosed by the invention, by introducing the path information of the triple and further considering the local and global importance of the path, the knowledge graph completion accuracy is improved.

Description

technical field [0001] The present disclosure relates to the technical fields of data mining and deep learning, and in particular to a convolutional network-based path-aware knowledge graph completion method and related equipment. Background technique [0002] Knowledge graph is a structured semantic knowledge base, which is used to quickly describe concepts and their relationships in the physical world. The knowledge graph extracts simple and clear triples (head entity h, relationship r, tail entity t) through effective processing, processing, and integration of intricate document data. For example, if the capital of China is Beijing, it can be extracted as a triplet (China, capital, Beijing). [0003] Knowledge Graph Completion aims to mine hidden triples based on known triples in knowledge graphs. As an important means to improve the integrity of knowledge graphs, it has attracted extensive attention from researchers at home and abroad. A large number of knowledge graph...

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Application Information

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IPC IPC(8): G06F16/36G06N3/04G06N3/08
Inventor 程祥苏森贾宁宁陈立炯
Owner BEIJING UNIV OF POSTS & TELECOMM