Keywords
risk assessment, ontology, operationalisation, knowledge integration, local knowledge
Start Date
1-7-2012 12:00 AM
Abstract
Integrated natural hazard risk assessment aims at capturing the impacts and diverse consequences of natural hazards on different types of elements at risk (i.e. evaluation criteria). Typically, a risk assessor selects such evaluation criteria relying on expert knowledge. Moreover, legal frameworks, best-practice guidelines as well as manifold requirements by stakeholders should be considered in the assessment process. This is also true for risk mapping. In order to provide useful deliverables for stakeholders, their preferences in regard to content and visualization have to be borne in mind. Developing tools to assist risk assessment may focus on some or all of these aspects. Necessary in this context are solutions which are capable of capturing relevant expert knowledge, but which are also easily adaptable to changing requirements. Ontologies, and knowledge bases built upon these, seem to pose ideal concepts to tackle this objective. Ontologies are formal, sharable and machine-interpretable knowledge representations. They thus seem suitable to capture explicit and formal expert knowledge on natural hazard risk assessment. Ontologies may also serve as knowledge stores for tacit information. This way, stakeholder preferences as well as local knowledge, a knowledge body often untapped, can be captured and made accessible. Ontology building methodologies provide the required means to structure and formalize tacit knowledge. They also allow for an iterative extension and adaption of an ontology as required. We present an ontology which captures core concepts of risk assessment and their relations as well as local knowledge and stakeholder preferences. The ontology is used to enhance a software tool which has previously been employed in flood risk assessment case studies. In doing so, we show how semantics can assist an integrated natural hazard risk assessment, and further evaluate the pros and cons of the proposed approach.
Operationalizing expert knowledge and stakeholder preferences in integrated natural hazard risk assessment
Integrated natural hazard risk assessment aims at capturing the impacts and diverse consequences of natural hazards on different types of elements at risk (i.e. evaluation criteria). Typically, a risk assessor selects such evaluation criteria relying on expert knowledge. Moreover, legal frameworks, best-practice guidelines as well as manifold requirements by stakeholders should be considered in the assessment process. This is also true for risk mapping. In order to provide useful deliverables for stakeholders, their preferences in regard to content and visualization have to be borne in mind. Developing tools to assist risk assessment may focus on some or all of these aspects. Necessary in this context are solutions which are capable of capturing relevant expert knowledge, but which are also easily adaptable to changing requirements. Ontologies, and knowledge bases built upon these, seem to pose ideal concepts to tackle this objective. Ontologies are formal, sharable and machine-interpretable knowledge representations. They thus seem suitable to capture explicit and formal expert knowledge on natural hazard risk assessment. Ontologies may also serve as knowledge stores for tacit information. This way, stakeholder preferences as well as local knowledge, a knowledge body often untapped, can be captured and made accessible. Ontology building methodologies provide the required means to structure and formalize tacit knowledge. They also allow for an iterative extension and adaption of an ontology as required. We present an ontology which captures core concepts of risk assessment and their relations as well as local knowledge and stakeholder preferences. The ontology is used to enhance a software tool which has previously been employed in flood risk assessment case studies. In doing so, we show how semantics can assist an integrated natural hazard risk assessment, and further evaluate the pros and cons of the proposed approach.
