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Representation learning method for knowledge graph with hierarchical relationship structure

A technology of hierarchical relationship and knowledge graph, which is applied in the field of knowledge graph to achieve the effect of improving predictive ability

Pending Publication Date: 2020-08-04
TIANJIN UNIV
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  • Abstract
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AI Technical Summary

Problems solved by technology

[0004] The above method models are only based on the visible facts in the knowledge map for embedding tasks

Method used

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  • Representation learning method for knowledge graph with hierarchical relationship structure
  • Representation learning method for knowledge graph with hierarchical relationship structure
  • Representation learning method for knowledge graph with hierarchical relationship structure

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Embodiment Construction

[0032] Certain terms are used, for example, in the description and claims to refer to particular components. Those skilled in the art should understand that hardware manufacturers may use different terms to refer to the same component. The specification and claims do not use the difference in name as a way to distinguish components, but use the difference in function of components as a criterion for distinguishing. As mentioned throughout the specification and claims, "comprising" is an open term, so it should be interpreted as "including but not limited to". "Approximately" means that within an acceptable error range, those skilled in the art can solve the technical problem within a certain error range and basically achieve the technical effect.

[0033] In the description of the present invention, it should be understood that the orientation or positional relationship indicated by the terms "upper", "lower", "front", "rear", "left", "right", horizontal" etc. are based on th...

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Abstract

The invention belongs to the technical field of knowledge graphs, and particularly relates to a representation learning method for a knowledge graph with a hierarchical relationship structure, which comprises the following steps: step 1, selecting a tree-shaped hierarchical relationship structure, and performing formalized description on the knowledge graph with hierarchical relationship structureinformation according to the structure; step 2, according to the content of the knowledge graph after formalization and the knowledge graph representation learning target considering the hierarchicalrelationship structure, projecting the relationship and the entity into a k-dimensional vector space, and constructing a TransHRS model to enable the model to meet the conditions of the hierarchicalrelationship structure knowledge graph; step 3, setting a loss function, and minimizing the value of the overall loss function of the model by training the TransHRS model; and step 4, iteratively updating an embedding result of the entities and the relationships in the knowledge graph. Compared with the prior art, the method has the advantages that the hierarchical relationship structure information is encoded, so that the prediction capability of the model is improved.

Description

technical field [0001] The invention belongs to the technical field of knowledge graphs, and in particular relates to a representation learning method with hierarchical relational structure knowledge graphs. Background technique [0002] In recent years, with the rapid development of artificial intelligence, knowledge graphs have become an important data source for artificial intelligence-related applications. Many typical knowledge graphs, such as Freebase, DBpedia, YAGO, and NELL, were created and successfully used in real-world applications ranging from semantic analysis and named entity disambiguation to information extraction and question answering systems. A typical knowledge graph can be seen as a multi-relational graph composed of many entities and relationships, in which nodes are used to represent entities, and edges are used to represent the relationship between entities. In addition, each edge in the multi-relationship graph can also be regarded as a triplet in ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/28G06N20/00
CPCG06F16/288G06N20/00
Inventor 王鑫张富翔
Owner TIANJIN UNIV
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