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Silvia Ansaldi
Department of Innovation Technologies, Inail, Monte Porzio Catone, 00077 Rome, Italy

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Journal article
Published: 28 July 2021 in Sustainability
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In European Seveso Legislation for the control of the hazard of major accidents (Directive 2015/12/UE), the Safety Management System SMS is an essential obligation for managers and the authorities are required to periodically verify its adequateness through periodical inspections at Seveso sites. One of the pillars of the SMS is the collection and analysis of documents on accidents, near misses, and possible anomalies, in order to identify weaknesses and implement continuous improvement. In Italy, for a few years, the documents, gathered from all Italian Seveso sites by the inspectors, have been archived and used for research purposes. The archive currently contains some 4000 reports, collected in 5 years by some 100 inspectors throughout Italy. This paper discusses in detail the challenges faced to extract the knowledge hidden in the documents and make it usable through the design of a robust model. For this aim, machine learning techniques have been used for preprocessing of the reports for extracting the concepts and their relations, organized into an entity-relation model. The effectiveness of this methodology and its potentiality are pointed out by investigating a few hot topics, exploiting the information contained in the repository.

ACS Style

Silvia Ansaldi; Patrizia Agnello; Annalisa Pirone; Maria Vallerotonda. Near Miss Archive: A Challenge to Share Knowledge among Inspectors and Improve Seveso Inspections. Sustainability 2021, 13, 8456 .

AMA Style

Silvia Ansaldi, Patrizia Agnello, Annalisa Pirone, Maria Vallerotonda. Near Miss Archive: A Challenge to Share Knowledge among Inspectors and Improve Seveso Inspections. Sustainability. 2021; 13 (15):8456.

Chicago/Turabian Style

Silvia Ansaldi; Patrizia Agnello; Annalisa Pirone; Maria Vallerotonda. 2021. "Near Miss Archive: A Challenge to Share Knowledge among Inspectors and Improve Seveso Inspections." Sustainability 13, no. 15: 8456.

Conference paper
Published: 01 January 2012 in Computer Vision
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Methods and technologies for risk management have been developed and consolidated over time in different sectors and to meet various needs. Recently, ISO organization published a set of documents for risk assessment. These guidelines are not specific to a particular sector, but can be undertaken by any public or private organization and can be applied to any type of risk. This paper presents a research work that aims to realize a knowledge-base for the development of a tool to support the identification of the most appropriate risk management methodology according to the specific characteristics of an organization.

ACS Style

Silvia Ansaldi; Marina Monti; Patrizia Agnello; Franca Giannini. An Ontology for the Identification of the most Appropriate Risk Management Methodology. Computer Vision 2012, 7567, 444 -453.

AMA Style

Silvia Ansaldi, Marina Monti, Patrizia Agnello, Franca Giannini. An Ontology for the Identification of the most Appropriate Risk Management Methodology. Computer Vision. 2012; 7567 ():444-453.

Chicago/Turabian Style

Silvia Ansaldi; Marina Monti; Patrizia Agnello; Franca Giannini. 2012. "An Ontology for the Identification of the most Appropriate Risk Management Methodology." Computer Vision 7567, no. : 444-453.