Knowledge graph representation learning method based on path tensor decomposition

A knowledge map and tensor decomposition technology, applied in special data processing applications, instruments, electrical digital data processing, etc., can solve problems such as path reliability and semantic combination without consideration, and achieve enrichment and perfection of knowledge map and training model precise effect

Inactive Publication Date: 2017-03-22
XIAMEN UNIV OF TECH
View PDF4 Cites 34 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the existing reasoning algorithms cannot solve long-path reasoning, and the problem of path reliability and semantic combination is not considered. At

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Knowledge graph representation learning method based on path tensor decomposition
  • Knowledge graph representation learning method based on path tensor decomposition
  • Knowledge graph representation learning method based on path tensor decomposition

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0024] The technical solutions and beneficial effects of the present invention will be described in detail below in conjunction with the accompanying drawings.

[0025] like figure 1 As shown, the present invention provides a knowledge map representation learning method based on path tensor decomposition. First, the entities and relationships in the knowledge map are embedded into the d-dimensional vector space by embedding to make it a vector matrix. Next, in the vector In the space, the PRA algorithm is used to find the relationship path between each entity pair, and the path tensor model is used to decompose the path, and the loss function value of the model is calculated. Updates are made until the updates converge to a value or until the maximum number of iterations is reached. The present invention specifically comprises the steps:

[0026] Step 1, convert the training set embedding to a low-dimensional continuous vector space

[0027] Extract the entity set, relation...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention discloses a knowledge graph representation learning method based on path tensor decomposition, comprising the following steps of: step 1, extracting an entity set, a relation set and a triple set in a knowledge graph, and embedding the entity set and the relation set which satisfy a triple into a low-dimensional continuous vector space; step 2, acquiring a path between entities through a PRA (Progressive Refinement Approach) algorithm; step 3, carrying out tensor decomposition on the path where all entities exist possibly, and calculating a decomposition loss function value; step 4, repeating step 3 until a convergent preset value or the maximum number of iteration is reached; step 5, if the maximum number of iteration or the convergent preset value is reached, calculating a next triple-related path, and repeating steps 2 to 4 until all triples of a training set are executed; and step 6, outputting the corresponding entity set and relation set in a training model. The representation learning method can improve the inference accuracy of knowledge discovery and enhance the prediction precision.

Description

technical field [0001] The invention belongs to the fields of knowledge representation, knowledge discovery and artificial intelligence, and in particular relates to a learning method for knowledge map representation based on path tensor decomposition. Background technique [0002] As a new knowledge representation method and data management mode, Knowledge Graph has important applications in natural language processing, question answering, information retrieval and other fields. The knowledge map is a structured semantic knowledge base, which is used to describe concepts and their relationships in the physical world in symbolic form. It mainly uses (head, relation, tail) triples for knowledge representation, head is the head entity, and tail Is the tail entity, relation is the relationship, and the entities are connected to each other through the relationship, forming a network knowledge structure. [0003] Knowledge graph reasoning is to establish a new relationship betwe...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G06F17/30
CPCG06F16/288G06F16/2237
Inventor 林开标朱顺痣吴运兵卢萍杨帆
Owner XIAMEN UNIV OF TECH
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap