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Knowledge Representation Learning Method Integrating Relationship Path, Type and Entity Description Information

A technology for relational paths and description information, applied in the field of knowledge representation learning of multi-source information fusion, it can solve the problems of poor discrimination of relational paths, waste of information, fuzzy relational path semantics, etc., and achieve the effect of improving discrimination and accurate semantics.

Active Publication Date: 2020-09-29
ZHEJIANG UNIV
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  • Claims
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AI Technical Summary

Problems solved by technology

For example, when using relational path information, the discrimination of relational paths is poor, which blurs the semantics of relational paths; when using type information, only the types of entities and relations are considered, but the types of relational paths are not considered; in the existing model At most, only one kind of additional information is used, and multi-source information is not used, resulting in a waste of information

Method used

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  • Knowledge Representation Learning Method Integrating Relationship Path, Type and Entity Description Information
  • Knowledge Representation Learning Method Integrating Relationship Path, Type and Entity Description Information
  • Knowledge Representation Learning Method Integrating Relationship Path, Type and Entity Description Information

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Embodiment Construction

[0047] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, and do not limit the protection scope of the present invention.

[0048] This embodiment proposes a knowledge representation learning method that integrates relationship path, type, and entity description information. It is a multi-source information fusion knowledge representation learning method. The overall flow chart is as follows figure 1 As shown, it is divided into two parts: data preprocessing and knowledge representation learning. Among them, the data preprocessing part extracts the structural information, relationship path information, type information and entity description information in the knowledge base, and initializes ...

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Abstract

The invention discloses a knowledge representation learning method fusing relationship path, type and entity description information. The method comprises the following steps: (1) preprocessing a knowledge base to extract structured information, relationship path information, type information and entity description information; (2) selecting a positive sample from the knowledge base, constructinga negative sample, calculating the loss of the structured information, the relationship path information, the type information and the entity description information corresponding to the positive andnegative samples; (3) updating parameters, a practice vector and a relationship vector of a model according to the loss; and (4) repeating the step (1) to the step (3) until the number of iterations reaches the preset maximum number of iterations, and outputting the vectors of the entity and relationship to realize the learning of the knowledge representation. According to the method, on the basisof using a relationship path, modelling is performed on the knowledge base by fusing multi-source information, so that the problem that the only the structured information is used in traditional knowledge representation learning method and multiple additional information is not adopted is solved.

Description

technical field [0001] The invention relates to the field of knowledge representation learning, in particular to a multi-source information fusion knowledge representation learning method. Background technique [0002] With the advent of the Web3.0 era, the knowledge base containing a large amount of structured knowledge has become an important part of many semantic applications (such as intelligent question answering and search services), and more and more enterprises and organizations are committed to constructing large knowledge bases. The structured knowledge in the knowledge base is represented in the form of triples (head entity, relation, tail entity). Although the existing knowledge base already contains a large number of triples, there are still missing entities and relations in the knowledge base because the information is massive and constantly changing. How to complete the missing relationship between entities in the knowledge base is a key issue in the construc...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F16/22G06F16/28
Inventor 陈岭崔军
Owner ZHEJIANG UNIV
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