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Multi-Scale Dynamic Convolutional Network for Knowledge Graph Embedding

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成果类型:
期刊论文
作者:
Zhang, Zhaoli;Li, Zhifei;Liu, Hai;Xiong, Neal N.
通讯作者:
Li, ZF
作者机构:
[Li, Zhifei; Zhang, Zhaoli; Liu, Hai] Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Hubei, Peoples R China.
[Xiong, Neal N.] Cent China Normal Univ, Natl Engn Lab Educ Big Data, Wuhan 430079, Hubei, Peoples R China.
通讯机构:
[Li, ZF ] C
Cent China Normal Univ, Natl Engn Res Ctr E Learning, Wuhan 430079, Hubei, Peoples R China.
语种:
英文
关键词:
Computational modeling;Convolution;Semantics;Predictive models;Feature extraction;Knowledge engineering;Computer architecture;Knowledge graphs;knowledge graph embedding;complex relations;link prediction;convolutional network
期刊:
IEEE Transactions on Knowledge and Data Engineering
ISSN:
1041-4347
年:
2022
卷:
34
期:
5
页码:
2335-2347
基金类别:
10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 61875068 and 61505064) 10.13039/501100012226-Fundamental Research Funds for the Central Universities (Grant Number: CCNU20ZT017 and CCNU2020ZN008)
机构署名:
本校为第一且通讯机构
院系归属:
国家数字化学习工程技术研究中心
摘要:
Knowledge graphs are large graph-structured knowledge bases with incomplete or partial information. Numerous studies have focused on knowledge graph embedding to identify the embedded representation of entities and relations, thereby predicting missing relations between entities. Previous embedding models primarily regard (subject entity, relation, and object entity) triplet as translational distance or semantic matching in vector space. However, these models only learn a few expressive features and hard to handle complex relations, i.e., 1-to-...

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