8
A. Chatzimichail et al.
The system is a unified semantic interoperability framework that uses fuzzy ontology
and consists of three layers. Each layer performs the following operations respectively: (a) storing of heterogeneous health records, (b) mapping local ontologies to
global ones, by using relevant algorithms or human expertise and (c) user interface
through which medical experts send queries.
2.1.5 Security—Safety
Ontologies have become a trend recently for making decisions easier during a climate crisis (such as floods, earthquakes, forest fires etc.). The enormous flow of
information from humans and sensors is one of the most difficult challenges that the
authorities face during such crisis events. A lightweight ontology was proposed in
[26] to manage climate crisis and combine all relevant aspects of crisis management:
crisis representation, sensor analysis, crisis incidents and related impacts as well as
unit allocation of first responders.
The authors of [27] have suggested a system that lies in the intelligent combination
of devices and human information against human and situational awareness, in order
to encourage security and a secure ecosystem for people. In order to make the use
of deductive reasoning over the gathered information feasible to tackle a range of
urgent situations, such as health-related problems and missing children in crowded
places, the DESMOS ontology has been developed that covers most of the principles
that are related to the identification of critical incidents and the implementation of
risk management processes.
Significant developments have recently been made in technology for autonomous
vehicles, without direct human control. The safety evaluation of the automated driving functions is an important subject in the automotive industry. Methodically defined
scenarios by experts can help to strengthen engineering and safety research. Numerous studies have shown that ontologies provide an effective context for various
autonomous vehicles’ applications. Several scenarios for the development of automated driving services are proposed in [28]. The authors of [29] concentrated on
designing a model for supporting vehicle communication. They described an ontology that encompasses all possible on-road scenarios. They derived situation-aware
routing protocols based on this model, and created simulated traffic and unique scenarios that are likely to cause accidents.
A research was performed on the detection of health hazards in metro construction
sites in [30]. Security risk detection in metro construction is a knowledge-intensive
process and is one of the most important tasks while managing risk. The information
is collected mainly in non-structured formats from various sources. Additionally,
each project typically develops its own information system to facilitate decision
making. In the study, an ontology offers a way to standardize and codify knowledge
related to safety risk that can also be distributed between different actors involved
in the project as well as between difference computer systems. The ontology can
also be used in the production of smart applications that can support the detection of
safety risk.
A. Chatzimichail et al.
The system is a unified semantic interoperability framework that uses fuzzy ontology
and consists of three layers. Each layer performs the following operations respectively: (a) storing of heterogeneous health records, (b) mapping local ontologies to
global ones, by using relevant algorithms or human expertise and (c) user interface
through which medical experts send queries.
2.1.5 Security—Safety
Ontologies have become a trend recently for making decisions easier during a climate crisis (such as floods, earthquakes, forest fires etc.). The enormous flow of
information from humans and sensors is one of the most difficult challenges that the
authorities face during such crisis events. A lightweight ontology was proposed in
[26] to manage climate crisis and combine all relevant aspects of crisis management:
crisis representation, sensor analysis, crisis incidents and related impacts as well as
unit allocation of first responders.
The authors of [27] have suggested a system that lies in the intelligent combination
of devices and human information against human and situational awareness, in order
to encourage security and a secure ecosystem for people. In order to make the use
of deductive reasoning over the gathered information feasible to tackle a range of
urgent situations, such as health-related problems and missing children in crowded
places, the DESMOS ontology has been developed that covers most of the principles
that are related to the identification of critical incidents and the implementation of
risk management processes.
Significant developments have recently been made in technology for autonomous
vehicles, without direct human control. The safety evaluation of the automated driving functions is an important subject in the automotive industry. Methodically defined
scenarios by experts can help to strengthen engineering and safety research. Numerous studies have shown that ontologies provide an effective context for various
autonomous vehicles’ applications. Several scenarios for the development of automated driving services are proposed in [28]. The authors of [29] concentrated on
designing a model for supporting vehicle communication. They described an ontology that encompasses all possible on-road scenarios. They derived situation-aware
routing protocols based on this model, and created simulated traffic and unique scenarios that are likely to cause accidents.
A research was performed on the detection of health hazards in metro construction
sites in [30]. Security risk detection in metro construction is a knowledge-intensive
process and is one of the most important tasks while managing risk. The information
is collected mainly in non-structured formats from various sources. Additionally,
each project typically develops its own information system to facilitate decision
making. In the study, an ontology offers a way to standardize and codify knowledge
related to safety risk that can also be distributed between different actors involved
in the project as well as between difference computer systems. The ontology can
also be used in the production of smart applications that can support the detection of
safety risk.