20. Evolution of GIS GIS users tend to develop their own data sets for many reason: 1. they may not know available existing data sets that could be appropriately used for their applications; 2. access to these data sets is difficult; 3. they are not used to sharing data sets with other sectors and/or organizations; 4. existing geospatial data sets stored in a certain GIS system may not be easily exported to another system.
21. Evolution of GIS As a result, the new age of GIS is still characterized by: 1. many actors involved in data collection and distribution; 2. a proliferation of GIS applications , product types, and formats; 3. duplication as a consequence of the difficulties to access the existing data, and the highly specific quality of the data collected; 4. increasing difficulty in the exchange and use of data that came from different organizations ;
27. Nebert, The SDI Cookbook Information Resource 1. Document Metadata Data Server Metadata Server 2. Publish User Registry 3. Register 4. Query 5. Access
49. Semantic Matching Neither a standard data format nor a common data model allows for the transfer of the meaning of information The more complex issue of what is represented instead of how it is represented needs to be addressed
50. Semantic Matching Users Table Vegetation Table Fauna … Logical Model Fauna wolf wildcat fox … Database Vegetation Conifers Hardwood Species, Mediterranean macchia… fauna forest live Conceptual Model Vegetation Fauna observer Real world
56. ONTOLOGY Fundamental steps in ontology building, represented through a layer cake (adapted from Cimiano, 2006).
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59. Building ontologies for disaster management: seismic risk domain Damage definition B. Murgante, G. Scardaccione, G. Las Casas
60. Building ontologies for disaster management: seismic risk domain Damage definition B. Murgante, G. Scardaccione, G. Las Casas
61. Building ontologies for disaster management: seismic risk domain Definition of emergency area and strategic elements B. Murgante, G. Scardaccione, G. Las Casas
62. Building ontologies for disaster management: seismic risk domain Relationship IS_A is a link generalization / specialization among entities Super-class entities generalize the sub-classes Sub-class entities are Super-class specialization Sub-classes inherit Super-class attributes B. Murgante, G. Scardaccione, G. Las Casas Attribute 1 Sub-class 1 Super-class Sub-class 2 Attribute 2 Attribute 3 IS_A
63. Super-class Sub-class Building ontologies for disaster management: seismic risk domain B. Murgante, G. Scardaccione, G. Las Casas
64. Relationship Contribuisce a Vulnerabilità Rischio sismico Building ontologies for disaster management: seismic risk domain B. Murgante, G. Scardaccione, G. Las Casas
66. Building ontologies for disaster management: seismic risk domain B. Murgante, G. Scardaccione, G. Las Casas
67. Building ontologies for disaster management: seismic risk domain B. Murgante, G. Scardaccione, G. Las Casas
68. Building ontologies for disaster management: seismic risk domain Representation of relationships between concepts B. Murgante, G. Scardaccione, G. Las Casas
69. Attributes inherited from type of vulnerability Specific attribute of physic vulnerability Defining properties Building ontologies for disaster management: seismic risk domain B. Murgante, G. Scardaccione, G. Las Casas
Oltre a formalizzazioni e reti semantiche towntology può utilizzare strumenti multimediali per migliorare la spiegazione dei concetti.
Protégé also allows to immediately visualize relationships among classes, sub-classes and instances different from the IS_A ones
Starting from concepts strictly related to risk (e.g. hazard, vulnerability, exposure), more abstract concepts have been treated, such as deferred vulnerability, accessibility, etc.. These concepts are difficult to be unambiguously defined and often they have different meanings in different contexts. For instance the term “damage” is linked to the terms “vulnerability” and “exposure” by a relationship of “is related to” type
In order to better analyse relationships among concepts, ontologies may be graphically represented by tree structures, where concepts are nodes and relationships are arches. In graphical representations of ontologies, it is possible to show relationships among concepts through proximity, connected lines or colour coding, as well as to visualize only a part of the ontological scheme.