A Knowledge Graph Representation Learning Method Fused with Entity Description and Type

Abstract

The present invention proposes a knowledge map representation learning method that combines entity description and type, including the following steps: Step S1: use the translation model to obtain the embedding of triplet entities, and use the relationship in the triplet as the head entity and tail entity Inter-translation operation to obtain the numerical vector representation of each triplet entity and relationship; Step S2: Use the Doc2Vec model to embed the text information described by the entity; Step S3: The entity embedding obtained through the Trans model is mapped to the entity hierarchy type Combine the matrix to get the embedding of the triplet entity type; step S4: connect all the representation vectors to get the final triplet entity vector, optimize the training model by stochastic gradient descent, and evaluate the effect. The method proposed by the present invention improves the semantic information represented by the triplet entity in the knowledge map through the entity description and the embedding of the entity type.

Description

technical field [0001] The present invention relates to the field of knowledge graph, in particular to a knowledge graph representation learning method integrating entity description and type. Background technique [0002] In 2012, Google proposed the concept of knowledge graph and applied it to its search engine. Since then, great progress has been made in the construction of large-scale knowledge graphs, and a large number of knowledge graphs have emerged, such as YAGO, DBpedia, and FreeBase. At present, knowledge graph plays an important role in many artificial intelligence applications, such as intelligent question answering, information recommendation, web search, etc. A knowledge graph is a structured semantic network that stores a large number of fact triples (head entity, relation, tail entity), usually simplified to (h, r, t). [0003] However, with the gradual expansion of the scale of knowledge graphs, the diversification of data types, and the increasingly comp...

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Problems solved by technology

[0006] The main purpose of the present invention is to overcome the above-mentioned defects in the prior art, and propose a knowledge map representation learning method that combines entity description and type, which can solve the problem that most current representation learning models only consider the triple information of the knowledge map. The rich text information and type information in the map have a low degree of fusion and a single fusion method, which can well reduce the ambiguity of entities and relationships, improve the accuracy of reasoning and prediction, and improve the semantic information represented by triplet entities in the knowledge map.

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