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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

Pending Publication Date: 2021-10-26
ZHEJIANG UNIV OF TECH
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  • Claims
  • Application Information

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Problems solved by technology

Link prediction not only needs to predict whether there is a relationship between two entities, but also needs to predict the specific type of relationship, which makes this work extremely challenging

Method used

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

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Embodiment Construction

[0143] The present invention will be further described below.

[0144] A knowledge map representation learning method based on entity and relationship encoding in a neural network, comprising the following steps:

[0145] The first step is to construct the target triplet from the knowledge base, and obtain all the path relationships between the head entity and the tail entity in the triplet, the process is as follows:

[0146] 1.1. Obtain the entity set E and relation set R in the knowledge base, and construct a triplet S={(h,r,t)|h,t∈E∧r∈R}, r is the entity h and t The direct relationship between, h is the head entity, t is the tail entity;

[0147] 1.2. Obtain all relation path sets P={p between h and t 1 ,p 2 ,...p N}, where p i Represents the i-th path in the path set P, N represents the number of relationship paths, and the i-th path between h and t is denoted as P i =i1 ,r i2 ,...,r iM , t>, M represents the number of relationships on this relationship path;

[...

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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.

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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Application Information

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IPC IPC(8): G06F16/36G06K9/62G06N3/04G06N5/02
CPCG06F16/367G06N5/022G06N3/044G06F18/22
Inventor 陆佳炜朱昊天王小定马超治王志鹏梅浩程振波张元鸣肖刚
Owner ZHEJIANG UNIV OF TECH
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