An ontology-based intelligent data query system in manufacturing networks

Facts can be exchanged in multiple fields with the help of disease-specific ontologies. A range of diverse values can be produced by mining ontological approaches for demonstrating disease mechanisms. Alzheimer’s disease (AD) is an incurable neurological brain illness. An early diagnosis of AD can be helpful for better treatment and the prevention of brain tissue destruction. Researchers have used machine learning techniques to predict the early detection of AD. However, Alzheimer’s disorders are still underexplored in the knowledge domain. In the biomedical field, the illustration of terminologies and notions is essential. Multiple methods are adopted to represent these notions, but ontologies are the most frequent and accurate. Ontology construction is a complex and time-consuming process. The designed ontology relies on Disease Ontology (DO), which is considered the benchmark in medical practice. Ontology reasoning mechanisms can be adopted for AD identification. In this paper, a deep convolutional neural network-based approach is proposed to diagnose Alzheimer’s disease, using an AD dataset acquired from Kaggle. Machine learning-based approaches (logistic regression, gradient boosting, XGB, SGD, MLP, SVM, KNN, random forest) are also used for a fair comparison. The simulation results are generated using three strategies (default parameters, 10-cross validation, and grid search), and MLP provides superior results on a default parameter strategy with an accuracy of 92.12%. Furthermore, the deep learning-based approach convolutional neural network (CNN) achieved an accuracy of 94.61%. The experimental results indicate that the construction of ontology, with the help of deep learning knowledge, can produce better results where the robustness and scalability can be enhanced. In comparisons to other methods, the CNN results are excellent and encouraging.

show abstract

“…In the random forest, a class prediction is displayed on every tree. The model's calculations are based on the class with the highest votes [33].…”

Section: Random Forest (Rf)mentioning

confidence: 99%

Constructing Domain Ontology for Alzheimer Disease Using Deep Learning Based Approach

Bangyal¹,

Rehman

Nawaz

et al. 2022

Electronics

View full text Add to dashboard Cite

show abstract

“…The authors in [23] have used the manufacturing resource, process, and product terms from the ISA-95 standard and semantic web rule Language (SWRL) to infer implicit in-formation, allowing them to check the machinery required to generate product variations to design flexible and information rich production models. Saeidlou, S. et al have designed an ontology model for the manufacturing domain and developed a semantic query algorithm to investigate the semantic richness of an intelligent data query system in the proposed ontology model [24]. Wan et al have proposed an ontology for resource configuration that has utilized web ontology language (OWL) to represent the domain knowledge of the reconfiguration of sensible manufacturing resources [25].…”

Section: A Ontologies For Smart Manufacturingmentioning

confidence: 99%

Semantic Modeling, Development and Evaluation for the Resistance Spot Welding Industry

et al. 2023

View full text Add to dashboard Cite

The ongoing industrial revolution termed Industry 4.0 (I4.0) has borne witness to a series of profound changes towards increasing smart automation, particularly in the industrial sectors of automotive, aerospace, manufacturing, etc. Automatic welding, a widely applied manufacturing process in these domains, is not an exception of these changes. One type of automatic welding, Resistance Spot Welding (RSW), lies in the center of this work. Large volumes and varieties of RSW data are being generated, thanks to the technologies behind I4.0. To address the associated data challenges, ontologies are essential in various aspects: integrating data sources, enhancing interoperability, and unifying knowledge etc. However, there have been limited studies around the semantic modelling of Resistance Spot Welding: Existing ontologies have overlooked some crucial concepts, such as an operation-centric view, welding software, and welding electrodes, which are essential for the monitoring of sensor measurements as well as the status of machine components (e.g., electrode wear). Additionally, current ontologies are not publicly available (to the best of our knowledge), and therefore cannot be accessed by other users. Such a lack of availability often requires that users build their own ontologies from scratch. In this paper, we propose our RSW ontology (RSWO) a to formalize knowledge in the RSW domain. It combines three sources of knowledge: extensive discussions with Bosch welding experts; reusing terminologies following ISO-14327 and ISO-14373 standards; and existing established ontologies. We have evaluated RSWO on real-world data from monitoring welding quality at Bosch in Germany, using Competency Questions, FAIR principles, OOPS!, and OntoMetrics.

show abstract

“…It interacts with customers to analyze their favorites and read behavior [36]. Saeidlou et al [37] intend to improve the indicative effect in depiction of member's query purpose and to permit members to query manufacturing messages on the basis of self-context. As for the portion of BN approach in the ontology mapping, it is an autonomous mode.…”

Section: Issues Of Ontology Developmentmentioning

confidence: 99%

Study on Ontological Knowledge Integration of Micromachining for Collaborative Process

Liang

2023

J. Adv. Manuf. Syst.

View full text Add to dashboard Cite

In collaborative manufacturing environment, the knowledge integration in collaborative process is very crucial, especially the heterogeneous information distributed among various cooperative suppliers. To tackle the optimization of knowledge consolidation in co-manufacturing, a knowledge integration with ontological framework is proposed in this study. First, by representing the collaborative business activities for cooperative suppliers, an architecture of knowledge integration with ontological schema is generated. The ontological model of knowledge concepts is explored in terms of the analysis of the ontological structure of knowledge. Aiming at the concept, an approach of ontology integration on the basis of ontological similarity is promoted. There are two portions included in this approach, mapping and combination, which are implemented through the analysis of similarity in two ontologies namely universal and regional. To integrate the two ontologies, the relations and restrictions among fields are definitely depicted by the Semantic Web Rule Language. Eventually, the presented framework is illustrated through a case study, and the outcomes satisfy the needs of knowledge combination to assure that the effectiveness of the presented method is confirmed.

show abstract

An ontology-based intelligent data query system in manufacturing networks

Cited by 12 publications

References 34 publications

Constructing Domain Ontology for Alzheimer Disease Using Deep Learning Based Approach

Constructing Domain Ontology for Alzheimer Disease Using Deep Learning Based Approach

Semantic Modeling, Development and Evaluation for the Resistance Spot Welding Industry

Study on Ontological Knowledge Integration of Micromachining for Collaborative Process

Contact Info

Product

Resources

About