# Ontological Representation of Smart City Data: From Devices to Cities
## 3 Ontology Characterization

Table 3: Ontology characterization. The results provided in this table suggest that the most reused ontologies are those stable recommendations and those published by standardization bodies like the W3C Semantic Sensor Network Ontology (_oldssn_) [27] ([[https://www.w3.org/2005/Incubator/ssn/ssnx/ssn](https://www.w3.org/2005/Incubator/ssn/ssnx/ssn)]([https://www.w3.org/2005/Incubator/ssn/ssnx/ssn](https://www.w3.org/2005/Incubator/ssn/ssnx/ssn))), OWL-Time [41] ([[http://www.w3.org/2006/time](http://www.w3.org/2006/time)]([http://www.w3.org/2006/time](http://www.w3.org/2006/time))), and geo [42] ([[https://www.w3.org/2003/01/geo/wgs84_pos#](https://www.w3.org/2003/01/geo/wgs84_pos#)]([https://www.w3.org/2003/01/geo/wgs84_pos#](https://www.w3.org/2003/01/geo/wgs84_pos#))). In addition, by looking at the domains of the ontologies commonly reused, it is possible to infer which are the domains usually needed in smart city use cases. The inferred domains and their related ontologies are listed as follows:  
* time: OWL-Time, timezone ([[http://www.w3.org/2006/timezone](http://www.w3.org/2006/timezone)]([http://www.w3.org/2006/timezone](http://www.w3.org/2006/timezone))), and timeline ([[http://purl.org/NET/c4](http://purl.org/NET/c4) dm/timeline.owl]([http://purl.org/NET/c4](http://purl.org/NET/c4) dm/timeline.owl)). * geospatial: geo, geosparql [43,44] ([[http://www.opengis.net/ont/geosparql](http://www.opengis.net/ont/geosparql)]([http://www.opengis.net/ont/geosparql](http://www.opengis.net/ont/geosparql))), and geonames ([[http://www.geonames.org/ontology](http://www.geonames.org/ontology)]([http://www.geonames.org/ontology](http://www.geonames.org/ontology))). * observations and measurements: QUDT, qu, muo,ucum, OM [45], oldssn, and om. * metadata: VANN ([[http://purl.org/vocab/vann](http://purl.org/vocab/vann)]([http://purl.org/vocab/vann](http://purl.org/vocab/vann))), dc [46] ([[http://purl.org/dc/elements/1.1](http://purl.org/dc/elements/1.1)]([http://purl.org/dc/elements/1.1](http://purl.org/dc/elements/1.1))), dcterms ([[http://purl.org/dc/terms](http://purl.org/dc/terms)]([http://purl.org/dc/terms](http://purl.org/dc/terms))), etc. In addition, other ontologies are reused to model very specific domains, for example goodrelations ([[http://purl.org/goodrelations/v1](http://purl.org/goodrelations/v1)]([http://purl.org/goodrelations/v1](http://purl.org/goodrelations/v1))) and FOAF ([[http://xmlns.com/foaf/0.1](http://xmlns.com/foaf/0.1)]([http://xmlns.com/foaf/0.1](http://xmlns.com/foaf/0.1))). Even though the metadata, upper level ontologies, and specific domains as persons and e-commerce do appear in the smart city ontologies analyzed, such domains are not considered as smart city core domains in the scope of this paper, since our focus is on these domains directly related to the Internet of Things. A similar work where a rank with the most relevant concepts extracted from the IoT ontologies is presented is [47]. These concepts also allows us to infer the same domains detected in this work. The second step of the characterization consisted of a further inspection of the ontologies' code in order to check the coverage of the domains for smart city ontologies in the IoT landscape. The results  
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of such inspection are shown in Table 5. The ontology code inspection was carried out in order to find classes, object properties, or data type properties related to each domain. For example, the event domain was detected in the _km4c_ ontology by means of the class km4 city:Event. This class has several data type properties such as km4 city:eventCategory, km4 city:eventTime, km4 city:placeName, etc., which allows representing the type of event, the starting time of the event, the location where the event takes place, etc.