research-article

DistRDF2ML - Scalable Distributed In-Memory Machine Learning Pipelines for RDF Knowledge Graphs

Authors:

Carsten Felix Draschner,

Farshad Bakhshandegan Moghaddam,

Hajira JabeenAuthors Info & Claims

CIKM '21: Proceedings of the 30th ACM International Conference on Information & Knowledge Management

Pages 4465 - 4474

https://doi.org/10.1145/3459637.3481999

Published: 30 October 2021 Publication History

Abstract

This paper presents DistRDF2ML, the generic, scalable, and distributed framework for creating in-memory data preprocessing pipelines for Spark-based machine learning on RDF knowledge graphs. This framework introduces software modules that transform large-scale RDF data into ML-ready fixed-length numeric feature vectors. The developed modules are optimized to the multi-modal nature of knowledge graphs. DistRDF2ML provides aligned software design and usage principles as common data science stacks that offer an easy-to-use package for creating machine learning pipelines. The modules used in the pipeline, the hyper-parameters and the results are exported as a semantic structure that can be used to enrich the original knowledge graph. The semantic representation of metadata and machine learning results offers the advantage of increasing the machine learning pipelines' reusability, explainability, and reproducibility. The entire framework of DistRDF2ML is open source, integrated into the holistic SANSA stack, documented in scala-docs, and covered by unit tests. DistRDF2ML demonstrates its scalable design across different processing power configurations and (hyper-)parameter setups within various experiments. The framework brings the three worlds of knowledge graph engineers, distributed computation developers, and data scientists closer together and offers all of them the creation of explainable ML pipelines using a few lines of code.

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Cited By

Färber MLamprecht DSusanti Y(2024)AutoRDF2GML: Facilitating RDF Integration in Graph Machine LearningThe Semantic Web – ISWC 202410.1007/978-3-031-77847-6_7(115-133)Online publication date: 27-Nov-2024
https://doi.org/10.1007/978-3-031-77847-6_7
Gergin BChelmis C(2024)SparkKG-ML: A Library to Facilitate End–to–End Large–Scale Machine Learning Over Knowledge Graphs in PythonThe Semantic Web – ISWC 202410.1007/978-3-031-77847-6_1(3-19)Online publication date: 27-Nov-2024
https://doi.org/10.1007/978-3-031-77847-6_1
Stodt FStodt JReich C(2023)Blockchain Secured Dynamic Machine Learning Pipeline for ManufacturingApplied Sciences10.3390/app1302078213:2(782)Online publication date: 5-Jan-2023
https://doi.org/10.3390/app13020782
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DistRDF2ML - Scalable Distributed In-Memory Machine Learning Pipelines for RDF Knowledge Graphs

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cover image ACM Conferences

CIKM '21: Proceedings of the 30th ACM International Conference on Information & Knowledge Management

October 2021

4966 pages

ISBN:9781450384469

DOI:10.1145/3459637

General Chairs:
Gianluca Demartini
The University of Queensland, Australia
,
Guido Zuccon
The University of Queensland, Australia
,
Program Chairs:
J. Shane Culpepper
RMIT University, Australia
,
Zi Huang
The University of Queensland, Australia
,
Hanghang Tong
University of Illinois at Urbana-Champaign, USA

Copyright © 2021 ACM.

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Published: 30 October 2021

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Cited By

Färber MLamprecht DSusanti Y(2024)AutoRDF2GML: Facilitating RDF Integration in Graph Machine LearningThe Semantic Web – ISWC 202410.1007/978-3-031-77847-6_7(115-133)Online publication date: 27-Nov-2024
https://doi.org/10.1007/978-3-031-77847-6_7
Gergin BChelmis C(2024)SparkKG-ML: A Library to Facilitate End–to–End Large–Scale Machine Learning Over Knowledge Graphs in PythonThe Semantic Web – ISWC 202410.1007/978-3-031-77847-6_1(3-19)Online publication date: 27-Nov-2024
https://doi.org/10.1007/978-3-031-77847-6_1
Stodt FStodt JReich C(2023)Blockchain Secured Dynamic Machine Learning Pipeline for ManufacturingApplied Sciences10.3390/app1302078213:2(782)Online publication date: 5-Jan-2023
https://doi.org/10.3390/app13020782
Abdallah HMansour E(2023)Towards a GML-Enabled Knowledge Graph Platform2023 IEEE 39th International Conference on Data Engineering (ICDE)10.1109/ICDE55515.2023.00225(2946-2954)Online publication date: Apr-2023
https://doi.org/10.1109/ICDE55515.2023.00225
Draschner CJabeen HLehmann J(2022)Ethical and Sustainability Considerations for Knowledge Graph based Machine Learning2022 IEEE Fifth International Conference on Artificial Intelligence and Knowledge Engineering (AIKE)10.1109/AIKE55402.2022.00015(53-60)Online publication date: Sep-2022
https://doi.org/10.1109/AIKE55402.2022.00015
Draschner CJabeen HLehmann J(2022)SimE4KG: Distributed and Explainable Multi-Modal Semantic Similarity Estimation for Knowledge Graphs2022 IEEE Fifth International Conference on Artificial Intelligence and Knowledge Engineering (AIKE)10.1109/AIKE55402.2022.00007(1-8)Online publication date: Sep-2022
https://doi.org/10.1109/AIKE55402.2022.00007

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