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A Search Methodology Based on Industrial Ontology and Machine Learning to Analyze Georeferenced Italian Districts

1
LUM Enterprise Srl, S.S. 100—Km 18, 70010 Bari, Italy
2
Università LUM “Giuseppe Degennaro”, S.S. 100—Km 18, 70010 Bari, Italy
*
Author to whom correspondence should be addressed.
Academic Editor: Jerry Chun-Wei Lin
Received: 23 March 2022 / Revised: 6 May 2022 / Accepted: 9 May 2022 / Published: 14 May 2022
The subject of the proposed study is a method implementable for a search engine able to provide supply chain information, gaining the company’s knowledge base. The method is based on the construction of specific supply chain ontologies to enrich Machine Learning (ML) algorithm results able to filter and refine the searching process. The search engine is structured into two main search levels. The first one provides a preliminary filter of supply chain attributes based on the hierarchical clustering approach. The second one improves and refines the research by means of an ML classification and web scraping. The goal of the searching method is to identify a georeferenced supply chain district, finalized to optimize production and planning production strategies. Different technologies are proposed as candidates for the implementation of each part of the search engine. A preliminary prototype with limited functions is realized by means of Graphical User Interfaces (GUIs). Finally, a case study of the ice cream supply chain is discussed to explain how the proposed method can be applied to construct a basic ontology model. The results are performed within the framework of the project “Smart District 4.0”. View Full-Text
Keywords: search engine; machine learning; georeferenced districts; supply chain ontology search engine; machine learning; georeferenced districts; supply chain ontology
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MDPI and ACS Style

Massaro, A.; Cosoli, G.; Magaletti, N.; Costantiello, A. A Search Methodology Based on Industrial Ontology and Machine Learning to Analyze Georeferenced Italian Districts. Knowledge 2022, 2, 243-265. https://0-doi-org.brum.beds.ac.uk/10.3390/knowledge2020015

AMA Style

Massaro A, Cosoli G, Magaletti N, Costantiello A. A Search Methodology Based on Industrial Ontology and Machine Learning to Analyze Georeferenced Italian Districts. Knowledge. 2022; 2(2):243-265. https://0-doi-org.brum.beds.ac.uk/10.3390/knowledge2020015

Chicago/Turabian Style

Massaro, Alessandro, Gabriele Cosoli, Nicola Magaletti, and Alberto Costantiello. 2022. "A Search Methodology Based on Industrial Ontology and Machine Learning to Analyze Georeferenced Italian Districts" Knowledge 2, no. 2: 243-265. https://0-doi-org.brum.beds.ac.uk/10.3390/knowledge2020015

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