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Steelmaking Predictive Analytics Based on Random Forest and Semantic Reasoning

Sadeer Beden, Kayal Lakshmanan, Cinzia Giannetti Orcid Logo, Arnold Beckmann Orcid Logo

Applied Sciences, Volume: 13, Issue: 23, Start page: 12778

Swansea University Authors: Sadeer Beden, Kayal Lakshmanan, Cinzia Giannetti Orcid Logo, Arnold Beckmann Orcid Logo

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DOI (Published version): 10.3390/app132312778

Abstract

This paper proposes a human-in-the-loop framework that integrates machine learning models with semantic technologies to aid decision making in the domain of steelmaking. To achieve this, we convert a random forest (RF) into rules in a Semantic Web Rule Language (SWRL) format and represent real-world...

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Published in: Applied Sciences
ISSN: 2076-3417
Published: MDPI AG 2023
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URI: https://cronfa.swan.ac.uk/Record/cronfa65211
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spelling v2 65211 2023-12-04 Steelmaking Predictive Analytics Based on Random Forest and Semantic Reasoning acf0be82092335f6fb65bb51f29c46ac Sadeer Beden Sadeer Beden true false 31fdeba4e76994bc72c5b8954389f8ab Kayal Lakshmanan Kayal Lakshmanan true false a8d947a38cb58a8d2dfe6f50cb7eb1c6 0000-0003-0339-5872 Cinzia Giannetti Cinzia Giannetti true false 1439ebd690110a50a797b7ec78cca600 0000-0001-7958-5790 Arnold Beckmann Arnold Beckmann true false 2023-12-04 SBI This paper proposes a human-in-the-loop framework that integrates machine learning models with semantic technologies to aid decision making in the domain of steelmaking. To achieve this, we convert a random forest (RF) into rules in a Semantic Web Rule Language (SWRL) format and represent real-world data as a knowledge graph in a Resource Description Framework (RDF) format, capturing the meta-data as part of the model. A rule engine is deployed that applies logical inference on the knowledge graph, resulting in a semantically enriched classification. This new classification is combined with external domain-expert knowledge to provide improved, knowledge-guided assistance for the human-in-the-loop system. A case study in the steel manufacturing domain is introduced, where this application is used for real-world predictive analytic purposes. Journal Article Applied Sciences 13 23 12778 MDPI AG 2076-3417 steelmaking; ontology; knowledge graphs; semantic reasoning; machine learning; random forest; predictive analytics 28 11 2023 2023-11-28 10.3390/app132312778 COLLEGE NANME Biosciences COLLEGE CODE SBI Swansea University This work was funded by Swansea University and the Engineering and Physical Sciences Research Council grants EP/T517537/1, EP/V061798/1, EP/S001387/1 & EP/S018107/1. 2024-04-09T15:24:37.5081921 2023-12-04T14:14:12.6737175 Faculty of Science and Engineering School of Biosciences, Geography and Physics - Biosciences Sadeer Beden 1 Kayal Lakshmanan 2 Cinzia Giannetti 0000-0003-0339-5872 3 Arnold Beckmann 0000-0001-7958-5790 4 65211__29196__ac1b8f78b58744c2aac804a6682df7f7.pdf 65211_ABeckmann.pdf 2023-12-04T14:23:07.1577460 Output 1563798 application/pdf Version of Record true © 2023 by the authors. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. true eng https://creativecommons.org/licenses/by/4.0/
title Steelmaking Predictive Analytics Based on Random Forest and Semantic Reasoning
spellingShingle Steelmaking Predictive Analytics Based on Random Forest and Semantic Reasoning
Sadeer Beden
Kayal Lakshmanan
Cinzia Giannetti
Arnold Beckmann
title_short Steelmaking Predictive Analytics Based on Random Forest and Semantic Reasoning
title_full Steelmaking Predictive Analytics Based on Random Forest and Semantic Reasoning
title_fullStr Steelmaking Predictive Analytics Based on Random Forest and Semantic Reasoning
title_full_unstemmed Steelmaking Predictive Analytics Based on Random Forest and Semantic Reasoning
title_sort Steelmaking Predictive Analytics Based on Random Forest and Semantic Reasoning
author_id_str_mv acf0be82092335f6fb65bb51f29c46ac
31fdeba4e76994bc72c5b8954389f8ab
a8d947a38cb58a8d2dfe6f50cb7eb1c6
1439ebd690110a50a797b7ec78cca600
author_id_fullname_str_mv acf0be82092335f6fb65bb51f29c46ac_***_Sadeer Beden
31fdeba4e76994bc72c5b8954389f8ab_***_Kayal Lakshmanan
a8d947a38cb58a8d2dfe6f50cb7eb1c6_***_Cinzia Giannetti
1439ebd690110a50a797b7ec78cca600_***_Arnold Beckmann
author Sadeer Beden
Kayal Lakshmanan
Cinzia Giannetti
Arnold Beckmann
author2 Sadeer Beden
Kayal Lakshmanan
Cinzia Giannetti
Arnold Beckmann
format Journal article
container_title Applied Sciences
container_volume 13
container_issue 23
container_start_page 12778
publishDate 2023
institution Swansea University
issn 2076-3417
doi_str_mv 10.3390/app132312778
publisher MDPI AG
college_str Faculty of Science and Engineering
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hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Biosciences, Geography and Physics - Biosciences{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Biosciences, Geography and Physics - Biosciences
document_store_str 1
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description This paper proposes a human-in-the-loop framework that integrates machine learning models with semantic technologies to aid decision making in the domain of steelmaking. To achieve this, we convert a random forest (RF) into rules in a Semantic Web Rule Language (SWRL) format and represent real-world data as a knowledge graph in a Resource Description Framework (RDF) format, capturing the meta-data as part of the model. A rule engine is deployed that applies logical inference on the knowledge graph, resulting in a semantically enriched classification. This new classification is combined with external domain-expert knowledge to provide improved, knowledge-guided assistance for the human-in-the-loop system. A case study in the steel manufacturing domain is introduced, where this application is used for real-world predictive analytic purposes.
published_date 2023-11-28T15:24:33Z
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score 11.016235

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