Sequence text information-combined knowledge graph expression learning method and device

Abstract

The invention provides a sequence text information-combined knowledge graph expression learning method and a knowledge graph expression learning device. According to the method, not only the ternary relation group information between entities is utilized, but also the sequence text information containing the entities in a designated corpus is fully utilized. An energy equation is constructed, so that the entities have different expression vectors in the structured ternary relation group information and the non-structured text information. Meanwhile, a marginal-based evaluation function is minimized, and the expression of structure-based entity vectors, text-based entity vectors and relation vectors is learned. Therefore, the expression learning effect of a knowledge graph is remarkably improved. According to the method and the device, the learned knowledge graph expression fully utilizes the sequence text information of entities contained in the corpus. Therefore, the higher accuracy can be obtained in the tasks such as ternary group relation classification, ternary group head and tail entity prediction and the like. The good practicability is achieved, and the expression performance of the knowledge graph is improved.

Description

technical field [0001] The invention belongs to the field of natural language processing and information extraction, and in particular relates to a knowledge map representation learning method and device combined with sequential text information. Background technique [0002] In the context of rapid social development, human beings are now in the era of information explosion, and massive physical knowledge and information are generated every day. This information is widely distributed on the Internet, and is usually generated and stored in unstructured forms such as text or pictures. However, with the increasing demand of users for effective information screening and induction on the Internet, how to obtain valuable information from massive data has become a difficult problem. Therefore, the knowledge map came into being. [0003] The knowledge graph represents all concrete things in the world (such as proper nouns such as people, place names, book titles, team names) and ...

AI Technical Summary

Problems solved by technology

[0006] The technical problem to be solved by the present invention is how to provide a knowledge map representation learning method combined with sequence text information, so as to solve the problem in the prior art that the sequence text information of entities contained in the corpus cannot be fully utilized, so as to improve the knowledge map. Indicates performance

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