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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 possibly 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 In Italy, for a few years, the documents, gathered from all Italian Seveso sites by the inspectors, have been archived and used for research purposes.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 five years by some 100 inspectors throughout Italy. The paper discusses in the 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 as a 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.

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In recent years, technological research have developed many innovative systems, aiming at exploiting the potential of "smart" technologies for the industrial and occupational safety. Many innovative smart systems are now coming to the market, promising major improvements at a very low cost. Decision makers are puzzled by the numbers and diversity of proposals. The choice is more difficult for the establishments featuring the hazard of major accidents, where the operators have to satisfy the requirements of the Seveso Directive. They have to evaluate if the proposed systems are ready to be used, if they are relevant for the control of major hazards, and if they are effective. The goal of the paper is to provide the decision makers with a tool to select systems suitable to be effectively appliedinto the industrial plants.