Rule and path combined knowledge graph representation learning method

Abstract

The invention discloses a rule and path combined knowledge graph representation learning method, which comprises the following steps of: firstly, extracting a logic rule from a knowledge graph, and encoding and representing the logic rule; then, on the basis of the rule subjected to encoding representation, completing a relation semantic combination operation in the relation path and establishingsemantic association between the relation pairs; and finally, in combination with the triplet, the relation path vector representation between the entities and the semantic association constraint between the relation vectors, jointly constructing an energy equation, and obtaining a minimum evaluation function. The method has the advantages that not only is the accuracy of relation representation improved, but also semantic association among the relations is established by the aid of rules, vector representation of the relations with the semantic association is constrained, more semantic information is added into the vector representation of the relations, and the precision of the vector representation of the relations is improved.

Description

technical field [0001] The present invention relates to the technical fields of natural language processing and knowledge graph, and more specifically relates to a knowledge graph combination representation learning method combining rules and paths. Background technique [0002] In recent years, with the rapid development of Internet technology and application models, the explosive growth of data has been triggered, which contains a lot of valuable knowledge; the knowledge map describes various concepts, entities and their relationships in a structured form, and integrates massive Information is expressed in a form that is closer to the human cognitive world. At present, knowledge graph has played an important role in semantic search, intelligent question answering system, data mining and other fields. [0003] The knowledge graph describes the massive and valuable knowledge in the database through the triple knowledge representation of (head entity, relationship, tail enti...

AI Technical Summary

Problems solved by technology

However, this method directly combines the vector representation of the relationship in the semantic combination operation of the relationship path, without considering that the combination of the relationship itself should be a semantic level operation, which limits the accuracy of the relationship path representation

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