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Knowledge graph link prediction method based on PtrransE model

A technology of knowledge graph and prediction method, applied in the field of knowledge graph link prediction based on PtransE model, to achieve the effect of increasing accuracy and improving representation precision

Inactive Publication Date: 2021-06-29
ZHEJIANG UNIV OF TECH
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Problems solved by technology

However, it only relies on relationships and uses specific entity information directly, so it still has certain limitations when deriving multi-step relationships.

Method used

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  • Knowledge graph link prediction method based on PtrransE model
  • Knowledge graph link prediction method based on PtrransE model
  • Knowledge graph link prediction method based on PtrransE model

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Embodiment Construction

[0104] The present invention will be further described below.

[0105] A method for predicting knowledge map links based on PtransE model, said method comprising the following steps:

[0106] The first step is to construct the target triplet from the knowledge base, and obtain all the path relationships between the head entity and the tail entity in the triplet, the process is as follows:

[0107] 1.1. Obtain the entity set E and relation set R in the knowledge base, and construct a triplet S={(h,r,t)|h,t∈E∧r∈R}, r is the entity h and t The direct relationship between, h is the head entity, t is the tail entity;

[0108] 1.2. Obtain all relation path sets P={p between h and t 1 ,p 2 ,...p N}, where p i Represents the i-th path in the path set P, N represents the number of relationship paths, and the i-th path between h and t is denoted as P i =i1 ,r i2 ,...,r iM , t>, M represents the number of relationships on this relationship path.

[0109] The second step is to pe...

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Abstract

A knowledge graph link prediction method based on a PtrransE model comprises the following steps: 1, constructing a target triple from a knowledge base, and obtaining all path relationships between a head entity and a tail entity in the triple; 2, carrying out the relation coding, expressing direct relations and all path relations in the target triad as vectors in combination with Word2vec, and inputting the vectors expressed by the path relations into LSTM for sequential coding; 3, performing entity type coding to obtain type context vectors of a head entity and a tail entity of the target triple; 4, calculating the reliability of the relation path, inputting the result into an energy function, and judging whether the triple is true or not. According to the method, the representation precision of the triad is improved, and the accuracy of a prediction result is improved.

Description

technical field [0001] This method involves a knowledge map link prediction method based on the PtransE model. Background technique [0002] The knowledge base organizes human knowledge into a structured knowledge system, which describes the relationship between entities in the real world. People spend a lot of energy building various structured knowledge bases, such as language knowledge base WordNet, world knowledge base Freebase, etc. Knowledge base is an important basic technology to promote the development of artificial intelligence disciplines and support intelligent information service applications (such as intelligent search, intelligent question answering, personalized recommendation, etc.). In order to improve the quality of information services, domestic and foreign Internet companies (especially search engine companies) have launched knowledge base products, such as Google Knowledge Graph, Microsoft BingSatori, Baidu Zhixin, and Sogou Zhicube. Behind the famous...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/28G06F16/36G06N3/04G06N3/08
CPCG06F16/288G06F16/367G06N3/049G06N3/08G06N3/045
Inventor 陆佳炜朱昊天王小定吴俚达徐俊程振波高飞肖刚
Owner ZHEJIANG UNIV OF TECH
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