A method and apparatus for processing knowledge map based on semi-supervised embedded representation model

Abstract

The invention discloses a knowledge map processing method and a device based on a semi-supervised embedded representation model, the method comprises calculating a first-order proximity and a second-order proximity of a knowledge map, calculating the supervisory loss of the first-order proximity and the supervisory loss of the second-order proximity, establishing a linear combination of the supervisory loss of the first-order proximity and the supervisory loss of the second-order proximity, optimizing the knowledge map under the condition of minimizing the linear combination, and the like. Bycalculating the first-order proximity between any two vertices in the knowledge map, and calculating the second-order proximity according to the neighborhood structure of any two vertices in the knowledge map, At the same time, the first-order proximity and the second-order proximity are considered to jointly optimize the knowledge map so as to retain the local and global structural information ofthe knowledge map, thereby overcoming the defects caused by the dependence of the knowledge map on the characteristics of each vertex and the lack of the relationship information among the vertices in the prior art. The invention is widely applied to the field of image recognition.

Description

Technical field [0001] The invention relates to the technical field of information processing, in particular to a knowledge graph processing method and device based on a semi-supervised embedded representation model. Background technique [0002] Knowledge Graph (Knowledge Graph) can not only express Internet information in a form closer to the human cognitive world, but also provides a way to better organize, manage, utilize, and integrate massive amounts of information across information sources. At present, knowledge graph technology is mainly used to support advanced applications such as automatic question answering, intelligent semantic search and recommendation systems. A typical knowledge graph describes the entities and their relationships in the physical world in symbolic form, and its basic components are usually expressed as triples (head entity, relationship, tail entity), namely (h, r, t). The entities are connected with each other through relationships, forming a n...

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Problems solved by technology

figure 1 Among them, the entity "Tom Hanks" plays the role in the movie "Sleepless in Seattle", and the entity "MegRyan" is actually an actor in this movie. However, it is difficult to predict the relationship between "MegRyan" and "Sleepless in Seattle" according to the existing knowledge graph technology. Whether there is a connection between them, because the existing knowledge graph embedding representation methods cannot make full use of the global structural information of "TomHanks" in this knowledge graph, because they only consider the neighborhood entities of "TomHanks"

according to figure 1 From this example, it can be concluded that in the existing knowledge map technology, because only the information of the neighborhood entities is used to learn the embedded representation of the knowledge map, its performance is limited, especially when dealing with specific tasks such as link prediction. obvious

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