Riccardo Patriarca
Tenure-Track Assistant Professor at Sapienza Univ Rome
Rome, Italy
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Riccardo Patriarca wants to manage risk. From making a pilot's cockpit safer during an emergency to minimizing adverse events on a process plant, he's studying ways to make things safer when interactions between humans and machines go awry. He's worked with organizations like INAIL, EUROCONTROL, and Australian airline QANTAS.
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A bridge towards future safety science: “MATCH (: SAFETY) - [r] - (: ‘KNOWLEDGE GRAPH’) RETURN r”
Have you ever wondered to which extent knowledge graphs are being employed for making our world safer? As safety scientists, the speakers of this talk wanted to answer this question. The session presents an overview on the use of knowledge graphs for solving safety-related problems. What does safety scientists want? Is it needed to have a strong IT background? What is the role of Neo4j? These are only some of the questions being answered in this short talk. The early results of a bibliometric study will be presented, trying to connect knowledge graph experts and the community of safety scientists.
Unleashing Safety Insights: Harnessing Near Misses Through Knowledge Graphs
In recent years, industrial safety has shown significant improvement, but achieving zero incidents remains a distant goal. Valuable insights can be gained from analyzing near misses, which are lower-consequence events. However, this analysis requires processing non-standardized data from various fragmented sources.
In this session, Francesco and Riccardo will propose a methodological solution that utilizes a knowledge graph to extract knowledge from near-miss reports. This approach allows you to learn about ontological safety explorative analysis, unlocking otherwise inaccessible information. The results prove the advantages of using a knowledge graph to support safety interventions and provide a comprehensive view of industrial process management.
How to Navigate the Complicated World of Modern Industrial Establishments
The popularity of System-Theoretic Accident Model and Process (STAMP) is increasing among both industrial and academic worlds, representing a powerful alternative to managing modern complex systems. However, a limitation arises when applying STAMP to large systems, as reading related STAMP schemes becomes difficult. The speakers of this session will present a solution to study these complicated STAMP models. The presented approach translates every STAMP scheme into a knowledge graph, enhancing its readability and the possibilities for analysis. A case study related to a big energy company will be presented to show the effectiveness of the proposed solution.
Other authors:
Silvia Maria Ansaldi - s.ansaldi@inail.it
Patrizia Agnello - p.agnello@inail.it
Riccardo Patriarca
Tenure-Track Assistant Professor at Sapienza Univ Rome
Rome, Italy
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