Centrality as a Method for the Evaluation of Semantic Resources for Disaster Risk Reduction

Abstract

Clear and straightforward communication is a key aspect of all human activities related to crisis management. Since crisis management activities involve professionals from various disciplines using different terminology, clear and straightforward communication is difficult to achieve. Semantics as a broad science can help to overcome communication difficulties. This research focuses on the evaluation of available semantic resources including ontologies, thesauri, and controlled vocabularies for disaster risk reduction as part of crisis management. The main idea of the study is that the most appropriate source of broadly understandable terminology is such a semantic resource, which is accepted by-or at least connected to the majority of other resources. Important is not only the number of interconnected resources, but also the concrete position of the resource in the complex network of Linked Data resources. Although this is usually done by user experience, objective methods of resource semantic centrality can be applied. This can be described by centrality methods used mainly in graph theory. This article describes the calculation of four types of centrality methods (Outdegree, Indegree, Closeness, and Betweenness) applied to 160 geographic concepts published as Linked Data and related to disaster risk reduction. Centralities were calculated for graph structures containing particular semantic resources as nodes and identity links as edges. The results show that (with some discussed exceptions) the datasets with high values of centrality serve as important information resources, but they also include more concepts from preselected 160 geographic concepts. Therefore, they could be considered as the most suitable resources of terminology to make communication in the domain easier. The main research goal is to automate the semantic resources evaluation and to apply a well-known theoretical method (centrality) to the semantic issues of Linked Data. It is necessary to mention the limits of this study: the number of tested concepts and the fact that centralities represents just one view on evaluation of semantic resources.

Importance of semantic interoperability in smart agriculture systems

“…no. Proposed concept Application/test scenario Remarks 95Based on the skills of an operator, the information is represented and subsume into the approach of service‐oriented orchestration for a production lineIndustrial automationThe information representation on the skills of an operator and it is involved in the service‐oriented orchestration method for a production line is described in the paper96Three stages have included in the strategy that is location aspect within each phase, international data standards, and finally Semantic Web Technologies, ontologies, and semantic rulesRoad networkThe gap of harmonization of data in road asset management in New Zealand and Australia is filled out97Semantic centrality (Outdegree, Indegree, Closeness, and Betweenness)Disaster risk managementThe evaluation of semantic resources is automated in the paper and allows to implement a well‐known theoretical technique to the linked data semantic issues98Semantic web‐based technologies. Specifically, RDF format is focused on linking and publishing od information for the purpose of sharingAgricultureTo share the data, RDF format is focused by the authors to publish and link the information that helps the farmers to access the related and contextual information accurately99For virtual reality and embedded systems, service‐oriented architecture is consideredES and VR applicationsIn the product life cycle, different scenarios are made by service‐oriented architectures.…”

Crisis Maps—Observed Shortcomings and Recommendations for Improvement

“…As a result, research has aimed at finding adequate methods for depicting objects and phenomena, specially adjusted to specific tasks and requirements at the technical, social, semantic, and cognitive levels [11][12][13][14][15][16], and for analysing and evaluating their success among users [17,18]. Cartography can be used successfully in crisis management.…”