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Dynamic knowledge graph representation learning method and system based on anchor points

A technology of knowledge graph and learning method, applied in the field of anchor-based dynamic knowledge graph representation learning method and system, which can solve the problems of low efficiency, time-consuming and laborious, and achieve the effect of entity alignment and relationship fusion.

Active Publication Date: 2020-05-29
CHINA UNIV OF GEOSCIENCES (WUHAN)
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

When new knowledge is introduced, these methods must first integrate all the knowledge together and retrain, which is inefficient, especially in the real world, where the existing map data is huge, and if each new knowledge is added, it must be relearned , very time-consuming

Method used

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  • Dynamic knowledge graph representation learning method and system based on anchor points
  • Dynamic knowledge graph representation learning method and system based on anchor points

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Experimental program
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Embodiment 1

[0034] Please refer to figure 1 , this embodiment provides an anchor-based dynamic knowledge map representation learning method, including the following steps:

[0035]S1. Construct the base coordinate system: use the complex network analysis method to find out the key knowledge entities that support the global information in the existing knowledge map as anchor points, and use the vector of the anchor point as the base vector to construct the base coordinates system.

[0036] Specifically, the knowledge map is analyzed by complex network partitioning technology, and the greedy algorithm that introduces the minimum point covering algorithm is used to find nodes (that is, entities) that have more information interactions with other nodes in the existing knowledge map and are scattered as anchor points; The greedy algorithm that introduces the minimum point covering algorithm is applicable to any data presented in the form of a network. The specific process of using the algorit...

Embodiment 2

[0054] This embodiment adopts the k-shell algorithm when selecting the anchor point, and all the other processes are the same as in embodiment one, and the specific process of the k-shell algorithm is:

[0055] (1) Calculate the degree of all entities in the existing knowledge graph;

[0056] (2) Take out all entities with a degree of 1, put them into the shell_1 layer, continue to calculate the degree of the remaining entities in the existing knowledge map, take out the entities with a degree of 1, and put them into the shell_1 layer again; repeat the above process until Entities with a degree of 1 are fetched;

[0057] (3) Take out all entities with a degree of 2, put them into the shell_2 layer, continue to calculate the degree of the remaining entities in the existing knowledge map, take out the entities with a degree of 2, and put them into the shell_2 layer again; repeat the above process until Entities with a degree of 2 are fetched;

[0058] (4) Calculate the degree ...

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Abstract

The invention provides a dynamic knowledge graph representation learning method and system based on anchor points, and the method comprises the steps: firstly finding key entities which play a role insupporting global information in an existing knowledge graph, and building a base coordinate system through the vectors of the entities; secondly, performing semantic alignment, including entity alignment and relationship fusion, on the newly added knowledge and the existing knowledge graph; finally, carrying out representation learning under a base coordinate system, so that only newly-added knowledge and related local knowledge of an existing knowledge graph need to be combined for training, a new knowledge entity is placed at a proper position in a knowledge space, and self-adaptive growthof the dynamic knowledge graph is achieved. The method has the beneficial effects that text information of entities and relationships is used as a semantic basis, and an information basis of knowledge fusion is provided, so that entity alignment and relationship fusion are more comprehensive and sufficient; a word2vec vector generation model is utilized to convert text information of entities andrelations into a vector form, so that the text information is used for mathematical operation.

Description

technical field [0001] The present invention relates to the field of dynamic knowledge graph representation learning, in particular to an anchor point-based dynamic knowledge graph representation learning method and system. Background technique [0002] Knowledge graphs are often expressed in the form of networks, where nodes represent entities, and edges represent the relationship between two entities. Each piece of knowledge can be represented in the form of a triple <head entity, relationship, tail entity>. Knowledge graphs are NLP technology in An important part of tasks such as intelligent question answering, web search and semantic analysis. Knowledge graphs are often huge in scale, containing hundreds of entities and billions of knowledge, but they are usually not complete enough, so knowledge graph completion is used to solve the problem of data sparsity in knowledge graphs. Based on symbolic representation methods such as triples, designers must design variou...

Claims

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Application Information

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IPC IPC(8): G06F16/36G06F16/38G06F40/284
CPCG06F16/367G06F16/381Y02D10/00
Inventor 赵东阳董理君李旦孙晨鹏陈仁谣
Owner CHINA UNIV OF GEOSCIENCES (WUHAN)
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