Knowledge graph representation learning method based on entity and relation coding in neural network

A learning method and neural network technology, applied in biological neural network models, knowledge expression, neural architecture, etc., to achieve the effect of improving accuracy and learning accuracy
CN113553441APending Publication Date: 2021-10-26ZHEJIANG UNIV OF TECH
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Patent Information

Authority / Receiving Office
CN · China
Current Assignee / Owner
ZHEJIANG UNIV OF TECH
Publication Date
2021-10-26

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Abstract

The invention relates to a knowledge graph representation learning method based on entity and relation coding in a neural network; the method comprises the following steps: step 1, constructing a target triple from a knowledge base, and obtaining all path relations between a head entity and a tail entity in the triple; step 2, carrying out relation coding; step 3, performing entity type coding; step 4, obtaining type context vectors of the head entity and the tail entity in the step 3, and inputting the type context vectors into the LSTM in sequence; step 5, forming path modes vrho (p) and vrho (r), and calculating the cosine similarity of the two path modes; and step 6, training a representation learning method. According to the method, the semantic information of the entities and the relationships is expressed, so that the entities, the relationships and the complex semantic association between the entities and the relationships are efficiently calculated.
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Description

technical field

[0001] The method relates to a knowledge map representation learning method based on entity and relation encoding in a neural network. Background technique

[0002] The knowledge graph was formally proposed by Google in June 2012. It is a graph-based data structure and a structured semantic knowledge base, which displays entities and their relationships in the real world in the form of graphs , and described in a formalized way, it is also a key resource for many artificial intelligence applications such as recommendation systems, intelligent question answering, and information retrieval. The knowledge graph is a carrier for storing structured objective factual information about people, things, and things in the real world. It is usually represented by triples as the basic structure. Each triple (h, r, t) contains the head entity h , the tail entity t and the relationship r between entities.

[0003] In recent years, people have constructed large-scale know...

Claims

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