Conference Paper/Proceeding/Abstract 644 views 119 downloads
Towards an Ontological Framework for Integrating Domain Expert Knowledge with Random Forest Classification
2023 IEEE 17th International Conference on Semantic Computing (ICSC)
Swansea University Authors: Sadeer Beden, Arnold Beckmann
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DOI (Published version): 10.1109/icsc56153.2023.00043
Abstract
This paper proposes an ontological framework that combines semantic-based methodologies and data-driven random forests (RF) to enable the integration of domain expert knowledge with machine-learning models. To achieve this, the RF classification process is firstly deconstructed and converted into se...
Published in: | 2023 IEEE 17th International Conference on Semantic Computing (ICSC) |
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Published: |
IEEE
2023
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Online Access: |
http://dx.doi.org/10.1109/icsc56153.2023.00043 |
URI: | https://cronfa.swan.ac.uk/Record/cronfa63104 |
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Abstract: |
This paper proposes an ontological framework that combines semantic-based methodologies and data-driven random forests (RF) to enable the integration of domain expert knowledge with machine-learning models. To achieve this, the RF classification process is firstly deconstructed and converted into semantic-based rules, which are combined with external rules constructed from the knowledge of domain experts. The combined rule set is applied to an ontological reasoner for inference, producing two classifications: (1) from simulating the selected RF voting strategy, (2) from the knowledge-driven rules, where the latter is prioritised. A case study in the steel manufacturing domain is presented that uses the proposed framework for real-world predictive maintenance purposes. Results are validated and compared to typical machine-learning approaches. |
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College: |
Faculty of Science and Engineering |