research-article

RWRel: A fast training framework for random walk-based knowledge graph embedding

Authors:

Qiu-Li ZhangAuthors Info & Claims

ACAI '21: Proceedings of the 2021 4th International Conference on Algorithms, Computing and Artificial Intelligence

Article No.: 67, Pages 1 - 6

https://doi.org/10.1145/3508546.3508613

Published: 25 February 2022 Publication History

Abstract

With the development of typical graph data scenarios such as social networks and recommender systems, the size of knowledge graphs is growing. In recent years, large-scale knowledge graphs have posed a challenge in terms of fast training of knowledge graph representation model when applied. The article proposes a fast training framework for random walk-based embedding of knowledge graphs (RWRel), which contains a random walk strategy based on relational paths and a relational encoding that introduces subject-object embeddings to model the subject-object semantics of relations in knowledge graphs. The experimental results show that the RWRel framework is effective in improving the speed and maintaining the performance of the representation learning method.

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Cited By

Sellami DInoubli WFarah IAridhi S(2025)Knowledge graph representation learning: A comprehensive and experimental overviewComputer Science Review10.1016/j.cosrev.2024.10071656(100716)Online publication date: May-2025
https://doi.org/10.1016/j.cosrev.2024.100716
Wang HChen YZhang CLi JGan CZhang YChen X(2022)GenGLAD: A Generated Graph Based Log Anomaly Detection FrameworkSmart Computing and Communication10.1007/978-3-031-28124-2_2(11-22)Online publication date: 18-Nov-2022
https://dl.acm.org/doi/10.1007/978-3-031-28124-2_2

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cover image ACM Other conferences

ACAI '21: Proceedings of the 2021 4th International Conference on Algorithms, Computing and Artificial Intelligence

December 2021

699 pages

ISBN:9781450385053

DOI:10.1145/3508546

Copyright © 2021 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 25 February 2022

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The Research Project of Ministry of Science and Technology of China

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ACAI'21

ACAI'21: 2021 4th International Conference on Algorithms, Computing and Artificial Intelligence

December 22 - 24, 2021

Sanya, China

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Overall Acceptance Rate 173 of 395 submissions, 44%

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Cited By

Sellami DInoubli WFarah IAridhi S(2025)Knowledge graph representation learning: A comprehensive and experimental overviewComputer Science Review10.1016/j.cosrev.2024.10071656(100716)Online publication date: May-2025
https://doi.org/10.1016/j.cosrev.2024.100716
Wang HChen YZhang CLi JGan CZhang YChen X(2022)GenGLAD: A Generated Graph Based Log Anomaly Detection FrameworkSmart Computing and Communication10.1007/978-3-031-28124-2_2(11-22)Online publication date: 18-Nov-2022
https://dl.acm.org/doi/10.1007/978-3-031-28124-2_2

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