Knowledge map representation learning method which combines entity and relationship description

A knowledge map and learning method technology, applied in special data processing applications, instruments, electrical digital data processing, etc., can solve problems such as the effective combination of text description information and structured information that cannot be entity and relationship, and achieve good practicability Effect

Active Publication Date: 2018-06-22
GUILIN UNIV OF ELECTRONIC TECH
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Problems solved by technology

[0005] What the present invention aims to solve is the problem that the existing knowledge map representation learning methods cannot effectively combine the text description

Method used

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  • Knowledge map representation learning method which combines entity and relationship description
  • Knowledge map representation learning method which combines entity and relationship description
  • Knowledge map representation learning method which combines entity and relationship description

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[0026] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with specific examples and with reference to the accompanying drawings.

[0027] Aiming at the problem that the existing knowledge graph representation learning method only considers the structured information of the triples in the knowledge graph, and does not effectively combine the text description information related to entities and relationships, the present invention fully considers the text of entities and relationships Description. The present invention uses a typical (head entity, relationship, tail entity) triple form combined with text description information to jointly express knowledge. figure 1 It is an example graph of typical triples in the knowledge graph. Among them, the nodes "Paris" and "France" represented by the box are the head entity and the tail entity, respectively, and...

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Abstract

The invention discloses a knowledge map representation learning method which combines an entity and a relationship description. Text description information of the entity and the relationship is takeninto consideration, two information sources of structured information and the text description information of a triple are well integrated, based on combined extracts of an end-to-end model of neuralnetworks conducted on the entity and the relationship, a balance factor is set to balance the structured information and the text description information, and different score functions are defined according to different prediction objects; a loss function is used for correlating an entity vector and a relationship vector, the loss function is optimized, and when an optimization goal is reached, the vectors of each entity and relationship in the knowledge map and the text description information can be learned. The method solves the sparsity and imbalance of entity and relationship in a knowledge base, accurately and effectively represents the interconnection between the entity and relationship, applies the interconnection to the large-scale knowledge map, and has good practicality.

Description

Technical field [0001] The present invention relates to the field of knowledge graphs and deep learning technology, in particular to a knowledge graph representation learning method that integrates entity and relationship descriptions. Background technique [0002] With the advancement of technology and the times, today's society is developing at an astonishing speed, and we are gradually entering an era of intelligence and information. Massive new data and diverse information appear in different forms every day. The Internet has become the most effective and convenient information acquisition platform in today's society. With the increasing demand of Internet users for real information acquisition, how to obtain more accurate and effective information from massive data has become the focus of attention in many fields, and the knowledge graph has also become the focus of attention in many fields. produce. [0003] Google introduced knowledge graphs into search engines in May 2012...

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Application Information

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IPC IPC(8): G06F17/30
CPCG06F16/367
Inventor 古天龙栗永芳常亮李凤英祝曼丽罗义琴
Owner GUILIN UNIV OF ELECTRONIC TECH
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