A knowledge base completion method based on variational interference and a tensor neural network

A neural network and knowledge base technology, applied in biological neural network models, knowledge expression, neural architecture, etc., can solve the problems of not considering the mutual information of hidden variables, ignoring prior knowledge, etc.

Active Publication Date: 2016-12-14
UNIV OF ELECTRONIC SCI & TECH OF CHINA
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Problems solved by technology

[0004] The purpose of the present invention is to solve the problem that the existing knowledge base completion technology ignores the prior knowledge of entities and relationships, and does not consider the pairwise interaction information between hidden variables, and proposes a variational inference based on Knowledge Base Completion Methods for Tensor Neural Networks

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  • A knowledge base completion method based on variational interference and a tensor neural network
  • A knowledge base completion method based on variational interference and a tensor neural network
  • A knowledge base completion method based on variational interference and a tensor neural network

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Embodiment Construction

[0048] The embodiments of the present invention will be further described below in conjunction with the drawings.

[0049] The invention provides a knowledge base completion method based on variational inference and tensor neural network, such as figure 1 As shown, including the following steps:

[0050] S1. According to the triples in the knowledge base (e i ,e j ,r k ), construct the tensor Y.

[0051] Where e i Represents the subject in the triplet, referring to the i-th entity; e j Represents the object in the triple, refers to the jth entity, r k Represents the ralation in the triple, referring to the k-th relationship.

[0052] Assuming that the number of entities in the knowledge base is N and the number of relations is M, the constructed tensor Y∈R N×N×M , R N×N×M Is the three-dimensional real number space with dimension N×N×M; if the triples (e i ,e j ,r k ) Exists, then the element y corresponding to the subscript in each dimension of the tensor Y ijk Is 1, otherwise y ijk...

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Abstract

The invention discloses a knowledge base completion method based on variational interference and a tensor neural network. Tensor decomposition thought and a Bayesian framework are introduced into the knowledge base completion method, and the prior knowledge of implicit variables is considered. The interaction between the implicit variables is discussed and nonlinear expression is performed through the neural network, and consideration of nondeterminacy is added. The accuracy of the knowledge base method is improved obviously, and compared with the existing technology, the knowledge base completion method has been greatly improved.

Description

Technical field [0001] The invention belongs to the technical field of knowledge base completion, and specifically relates to the design of a knowledge base completion method based on variational inference and tensor neural network. Background technique [0002] Knowledge base completion is an important research topic in the knowledge base, and it plays an important role in some applications such as question answering systems and information retrieval. The knowledge base uses triples (subject, relation, object) to represent data information, which is a kind of semantic network that reveals between entities. Although a large amount of information is stored, a considerable part of the information is missing and implicit in it Therefore, when the user searches, the corresponding answer may not be found in the knowledge base for the request that may be sent. In order to solve this problem, related researchers have proposed a series of learning algorithms based on the existing triples...

Claims

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Application Information

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IPC IPC(8): G06N5/02G06N3/04
CPCG06N5/022G06N3/047
Inventor 徐增林贺丽荣刘斌李广西盛泳潘王雅芳
Owner UNIV OF ELECTRONIC SCI & TECH OF CHINA
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