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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
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa65211
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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 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.
Keywords: steelmaking; ontology; knowledge graphs; semantic reasoning; machine learning; random forest; predictive analytics
College: Faculty of Science and Engineering
Funders: 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.
Issue: 23
Start Page: 12778

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