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Knowledge graph path reachability prediction method based on attention mechanism

A knowledge map and attention technology, applied in neural learning methods, biological neural network models, structured data retrieval, etc., to achieve the effects of improving utilization, increasing accuracy, and improving accuracy

Pending 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 path reachability prediction method based on attention mechanism
  • Knowledge graph path reachability prediction method based on attention mechanism
  • Knowledge graph path reachability prediction method based on attention mechanism

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Embodiment Construction

[0101] The present invention will be further described below.

[0102] A method for analyzing the accessibility of a knowledge map path based on an attention mechanism, comprising the following steps:

[0103] 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:

[0104] 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 and t is the tail entity.

[0105] 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;

[0106] The ...

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Abstract

A knowledge graph path reachability analysis method based on an attention mechanism includes the following steps: 1, constructing a target triple from a knowledge base, and obtaining all path relations between a head entity h and a tail entity t in the triple; 2, carrying out relation coding; 3, performing entity type coding; 4, repeating the step 2 and the step 3 to calculate a global path mode formed by combining all path modes, calculating an energy function of a triad formed by a head entity h, a direct relation r and a tail entity t, calculating the probability whether the direct relation r can connect the head entity and the tail entity, multiplying the energy function by the probability whether the energy function can be linked, and judging whether the triple is true or not. According to the method, the utilization rate of entities and relationships is improved, the accuracy of a probability calculation result is improved through an attention mechanism, the accuracy of vectors represented by triples is improved, and the accuracy of a result for predicting whether the entities can be mutually connected or not is improved.

Description

technical field [0001] This method involves a method for analyzing the accessibility of knowledge graph paths based on attention mechanism. 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 Sogo...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/28G06F16/36G06N3/04G06N3/08
CPCG06F16/288G06F16/367G06N3/049G06N3/08G06N3/048G06N3/045
Inventor 陆佳炜朱昊天王小定郑嘉弘张元鸣徐俊肖刚
Owner ZHEJIANG UNIV OF TECH
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