A Survey on Semantic Sensor Web: Sensor Ontology, Mapping and Query <s> (3) Extending by Combining with the other Ontology to Solve Actual Problem <s> Sensing devices are increasingly being deployed to monitor the physical world around us. One class of application for which sensor data is pertinent is environmental decision support systems, e.g. flood emergency response. However, in order to interpret the readings from the sensors, the data needs to be put in context through correlation with other sensor readings, sensor data histories, and stored data, as well as juxtaposing with maps and forecast models. In this paper we use a flood emergency response planning application to identify requirements for a semantic sensor web. We propose a generic service architecture to satisfy the requirements that uses semantic annotations to support well-informed interactions between the services. We present the SemSor- Grid4Env realisation of the architecture and illustrate its capabilities in the context of the example application. <s> BIB001 </s> A Survey on Semantic Sensor Web: Sensor Ontology, Mapping and Query <s> (3) Extending by Combining with the other Ontology to Solve Actual Problem <s> This paper describes the process followed in order to make some of the public meterological data from the Agencia Estatal de Meteorologia (AEMET, Spanish Meteorological Office) available as Linked Data. The method followed has been already used to publish geographical, statistical, and leisure data. The data selected for publication are generated every ten minutes by the 250 automatic stations that belong to AEMET and that are deployed across Spain. These data are available as spreadsheets in the AEMET data catalog, and contain more than twenty types of measurements per station. Spreadsheets are retrieved from the website, processed with Python scripts, transformed to RDF according to an ontology network about meteorology that reuses the W3C SSN Ontology, published in a triple store and visualized in maps with Map4rdf. <s> BIB002
Because the data of Semantic Sensor Web is linked-data, the ontology combination can make the application range greatly extend. The combination of SSN ontology and the other ontologies is necessary. Alasdair J G Gray BIB001 proposed SemSorGrid4Env ontology network to apply in flood emergency prediction scheme. This ontology network is composed of different ontologies. It is divided into four layers: the ontology of the specific fields, the information ontology, the upper ontologies and the external ontologies. These ontologies meet different knowledge representation requirements and they share and reuse between each other. SSN ontology reuses the DOLCE + DnS UltraLite and SWEET upper ontology. The ontologies in the flood field are the basis of SSN ontology. They also reuse the external ontology. These ontologies combine with each other and play the biggest role in order to solve the flood emergency prediction. AEMET ontology is also formed by multiple ontologies BIB002 . The ontology includes four modules: sensor, time, location and measurement. The Time module reuses the OWL Time Ontology, and the location Ontology reuses part of geobuddies ontology network, and the measure ontology reuses the concepts of SSN Ontology. All these ontologies are used to the meteorological forecasting of the Spanish meteorological bureau. They can transform the meteorological data to linked-data and have more comprehensive description.