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.
Highlights
Disaster risk reduction activities consist of collecting, processing, and visualizing large spatial data sets [1,2,3,4] which can be created as a combination of existing data with links to other data (Linked Data approach [5,6,7])
This study focuses on geographic and geography-related concepts [10] used in the disaster risk reduction domain
Geography and related disciplines motivated by the very important role of geography dealing with spatial information play a crucial role in crisis management and disaster risk reduction [1,2,3,4,8,9], because knowledge related to localization or position are crucial for all crisis management and risk reduction activities, and geography is essential in the Linked Data space [11]
Summary
Disaster risk reduction activities consist of collecting, processing, and visualizing large spatial data sets [1,2,3,4] which can be created as a combination of existing data with links to other data (Linked Data approach [5,6,7]). The Linked Data approach is one of the most efficient to deal with spatial data in terms of data volume, speed of processing, or intelligibility of data presentation and visualization. Linked Data, semantics (which is an integral part of Linked Data), and relevant tools (thesauri, ontologies, knowledge bases, controlled vocabularies, etc.) can contribute to one of the main tasks of disaster risk reduction as well as early warning activities. This task is connected with the necessity of fast communication, intelligibility, and common understanding of essential concepts, including their machine processing, or the development of advanced tools such as decision support systems [8,9]. Geographical data are a very important part of the Linking Open Data cloud diagram, which contains specific resources of spatial and geographic data (such as GeoNames.org or LinkedGeoData.org), but other very important Linked Data resources (such as DBpedia, AGROVOC, or Wikidata) include spatial components (for example, data with coordinates or geographical concepts)
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