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A knowledge graph representation method based on multi-core Gaussian distribution

A knowledge map and Gaussian distribution technology, applied in the creation of semantic tools, unstructured text data retrieval, etc., can solve problems such as semantic information confusion, achieve better results, simplify the training process, and resolve semantic ambiguity

Active Publication Date: 2021-08-06
GUANGDONG UNIV OF TECH
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  • Abstract
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  • Claims
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Problems solved by technology

But in fact, an entity may have different semantics. For example, "apple" may represent a company or a fruit. These are two completely different semantics. Only one point will cause semantic information confusion.

Method used

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  • A knowledge graph representation method based on multi-core Gaussian distribution
  • A knowledge graph representation method based on multi-core Gaussian distribution
  • A knowledge graph representation method based on multi-core Gaussian distribution

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Embodiment Construction

[0020] The specific embodiment of the present invention will be further described below in conjunction with accompanying drawing:

[0021] Such as figure 1 As shown, a knowledge map representation method based on multi-core Gaussian distribution, including negative sample sampling, multi-core Gaussian distribution of entity and relationship representation, using the translation distance model based on translation ideas to learn the representation of entities and relationships, specifically includes the following steps :

[0022] S1), randomly initialize entities and relationships, assuming that the knowledge map is composed of entity sets S=(s 1 ,s 2 ,...,s n ) and relation set R=(r 1 , r 2 ,...,r z ), where n represents the number of entities and z represents the number of relationships;

[0023] In the knowledge graph, each fact is represented by a triplet (source entity h, relation r, target entity t);

[0024] Each entity in the knowledge graph has k semantics, and...

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Abstract

The present invention relates to the field of deep learning technology, specifically a knowledge map representation method based on multi-core Gaussian distribution, including negative sample sampling, multi-core Gaussian distribution of entity and relationship representation, and using a translational distance model based on translation ideas to represent entities and relationships To learn, the present invention uses multi-core Gaussian distribution to represent each entity in the knowledge map, considering the characteristics of entities with multiple semantics and the natural attributes of each semantic range, and to a certain extent solves the problem of entities in the knowledge map because The problem of semantic ambiguity caused by multi-semantic features also improves the shortcomings of traditional methods that do not consider the scope of semantics to a certain extent. At the same time, compared with other methods, the training process of this method is simple and the effect is better.

Description

technical field [0001] The invention relates to the technical field of deep learning, in particular to a knowledge map representation method based on multi-core Gaussian distribution. Background technique [0002] In recent years, the research on artificial intelligence and knowledge graph has attracted much attention. The method of using knowledge graph as a known knowledge and combining various deep learning algorithms to improve the effect of various artificial intelligence tasks is also a research hotspot in recent years. , such as robotic systems based on knowledge graphs. The effective expression of knowledge graphs is the basic work of research and utilization of knowledge graphs. It can associate messy data and organize them into structured knowledge for users. This feature determines that it will have important applications in many fields. For example, existing search engines search based on keyword matching, and when the knowledge graph is established, after enter...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F16/36
Inventor 郝志峰柯妍蓉蔡瑞初陈炳丰
Owner GUANGDONG UNIV OF TECH
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