Short Paper: Semantic Annotations for Sensor Open Data

Abstract

Since the creation of the Open Data Euskadi (ODE) initiative in 2009, one of its challenges has been the publication of government Open Data following the Linked Data principles. On the other hand, one of the challenges for the Semantic Sensor Web is the integration and fusion of data from heterogeneous sensor networks. In this short paper we present the e orts made at the Bizkaisense1 project on the alignment of di erent ontologies with the objective to ful l these two challenges.

1 Introduction

Many governments across the world have realised the importance of opening data both as a service to promote transparency and as a way to enable businesses to make a better use of publicly available information. Open Data Euskadi2 is a good example of it, in this case fostered by the Basque Country Government in Spain. Despite the e orts made by this initiative towards Linked Data, they have been mainly focused on the publication of raw data, directly taken from the computer systems from di erent administrations. The fact is that there is a limited number of datasets published as Linked Data or, at least, in any of the RDF serializations; but the good news is that external providers such as research centres or companies can treat raw data and publish them as Open Linked Data.

One of the datasets requiring this treatment is the one containing the data generated by the pollution sensors deployed throughout Basque Country3. According to [ 4 ] there are ve challenges for the Semantic Sensor Web: 1) the rst is about the abstraction level of the data extraction, process and management; 2) quality and Quality of Service of sensor data; 3) integration and fusion of sensor data; 4) identi cation and location of relevant sensor-based data sources; and 5) rapid development of applications. The Bizkaisense project is focused on the accomplishment of the third challenge. We think that the rst step to integrate this Sensor Open Data with heterogeneous data sources is to publish them as Linked Data. In this short paper we present the e orts made at this rst step, which are related to the mapping of the raw data to appropriate ontologies and the alignment between them.

The remainder of the paper is organized as follows. Section 2 discusses related work. Section 3 exposes the Open Data available about pollution sensors. Section 4 exposes the adopted solution to map the raw data to appropriate ontologies. Finally, Section 5 concludes and outlines the future work. 2

Related Work

Several e orts have been done in the eld of semantic sensor networks; in this section we expose some examples. In [ 2 ], the SSN and SWEET ontologies are used to model sensor data and to allow a federated query system among them. However, these approaches do not use any ontology to represent the units of the measurements made by sensors. [ 3 ] presents a survey about the di erent ontologies to model di erent aspects of sensor networks. Linked Stream Middleware (LSM) provides wrappers for real time data collecting and publishing, a web interface to visualize and publish data and a SPARQL endpoint for querying data from heterogeneous sensor networks in an uni ed way [ 7 ]. In LSM, the user can import di erent ontologies to represent her/his sensors. This approach allows to annotate a wide variety of sensors, however, it can be an obstacle to manage the interoperability among di erent sensor networks. Related with the usage of custom ontologies, [ 1 ] uses its own ontology to represent the location of the sensors with a high granularity ( oor, room, etc.). In [ 5 ], they map the sensors of Android powered smartphones to SSN and DUL ontologies, extending SSN with the proper instances to represent these sensors. The approach presented by [ 10 ] extends SSN with a collection of observations that their sensor network observes.

The AEMET Linked Data4 project has a strong relationship with our Bizkaisense project. In this project the weather stations of AEMET (Agencia Estatal de Meteorolog a) have been annotated semantically. The data related to these stations have been extracted from CSV les provided by AEMET5. To annotate these weather stations, they have combined SSN ontology with aemetonto, a custom ontology made for the project which is used to represent the di erent meteorological phenomena that the stations can measure.

Summarizing, we can see that although there are many projects related to semantic sensor networks, usually, the way to ful l the limitations of the ontologies is to introduce new custom ontologies, instead of reingenieering existing ontological resources. Similar reingeneering work can be seen at [ 5 ] or [ 10 ]. We think that this second approach, which is adopted by Bizkaisense, is more suitable to achieve the interoperability among heterogeneous sensor networks. 4 http://aemet.linkeddata.es/ 5 ftp://ftpdatos.aemet.es/datos_observacion/observaciones_diezminutales/

Pollution Sensoring in Basque Country

There are 72 pollution sensors set up along the Basque Country, managed by the Basque Government. These sensors measure the air quality based on: chemical substances (xylene, sulfur dioxide, toluene, carbon monoxide, ozone, particulate matter (10 and 2,5 g=m3), nitrogen dioxide, hydrocarbons, hydrogen sul de, ammonia, ethylbenzene, volatile organic compounds, benzene and smoke) and solar and ultraviolet radiation. They also measure di erent atmospheric and meteorological phenomena like wind speed and direction, temperature, barometric pressure and humidity. The data generated by these sensors is very useful, for example, to track the evolution of the air quality over time. However, the provided raw data is very di cult to analyse without the possibility of making complex queries over it. To solve this issue, all of these features have been semantically annotated as can be seen at Section 4.

The data gathered by these sensors can be accessed in two di erent ways: 1) through the historical records stored into CSV les since 2000 and 2) through real-time data published at each sensor's web page6 extracted via web-scraping techniques. Two simple Python scripts have been built to parse both data sources. The generated RDF data is stored into an OpenLink Virtuoso semantic store and served through Pubby Linked Data interface. We do not go into this process of data transformation and publication in any depth because this paper focuses into the work done with ontologies used along the project. 4

Semantic Annotation of Pollution Sensors

Di erent ontologies have been used to semantically annotate these sensors' data, as depicted in Figure 1. The main ontology of the model is SSN (Semantic Sensor Network Ontology) [ 8 ]. This ontology, developed by the W3C Semantic Sensor Incubator Group, is used in Bizkaisense to annotate di erent aspects of sensors and their measurements. The location of a sensor is represented by an instance of the Point class from WGS84 Vocabulary7 through dul:hasLocation property, and it is linked with the nearest Feature instance of GeoNames8 through dul:nearTo property.

More reingenieering work has been done extending SWEET and MUO ontologies. SWEET 2.3 (Semantic Web for Earth and Environmental Terminology) is a collection of ontologies that describes both orthogonal concepts (space, time, physical quantities, etc.) and integrative science knowledge concepts (phenomena, events, etc.) [ 9 ]. Although the ontology is very complex and describes a wide variety of chemical substances, there are some concepts that it does not include. Concretely, we have extended the ontology9 with classes representing Ethylbenzene (C6H5C2H5) and HydrogenSul de (H2S), as can be seen in Figure 2. 6 http://www.ingurumena.ejgv.euskadi.net 7 http://www.w3.org/2003/01/geo/ 8 http://www.geonames.org/ 9 http://helheim.deusto.es/bizkaisense/sweetAll-extended.owl ssn:hasValue ssn:Observation

ssn:observedBy ssn:observedProperty ssn:observes ssn:Sensor ssn:ObservationValue sweet:Substance

wgs84_pos:Point dul:isClassifiedBy dul:nearTo

dul:hasLocation gn:Feature

Regarding to MUO ontology (Measurement Units Ontology)10, we have extended its instances11 from the data extracted from UCUM (Uni ed Code for Units of Measure)12. These new instances are cubic-meter, cubic-squared, microgram, millibar, milligram, milliwatt, meter-per-second, micro-gram-per-cubicmeter, milligram-per-cubic-meter, milliwatt-per-squared-meter, watt-per-squaredmeter and watt-per-squared-meter, as can be seen in Figure 3.

Finally, the Dublin Core [ 6 ] vocabulary is used to annotate common attributes like dates, titles, descriptions and so on. Code 1 shows an example of an observation made by a pollution sensor. More examples can be found at the project web page13 and its SPARQL endpoint14. 10 http://idi.fundacionctic.org/muo/muo-vocab.html 11 http://helheim.deusto.es/bizkaisense/ucum-ext.owl 12 http://idi.fundacionctic.org/muo/ucum-instances.html 13 http://helheim.deusto.es/bizkaisense/ 14 http://helheim.deusto.es/bizkaisense/sparql muo:DerivedUnit muo:SimpleDerivedUnit muo:ComplexDerivedUnit milliwatt microgram cubic-meter mil iwat-per-meter-squared micro-gram-per-cubic-meter milligram millibar squared-meter watt-per-squared-meter mil igram-per-cubic-meter meter-per-second

Code 1 Example of an observation made by a pollution sensor. @prefix bizkaisense: <http://helheim.deusto.es/bizkaisense/resource/station/> . @prefix observation: <.../bizkaisense/resource/station/ELCIEG/NO/01012011/00#> . @prefix ssn: <http://purl.oclc.org/NET/ssnx/ssn#> . @prefix sweet: <http://http://sweet.jpl.nasa.gov/2.3/propSpeed.owl#> . @prefix dul: <http://www.loa-cnr.it/ontologies/DUL.owl#> . @prefix ucum-extended: <http://helheim.deusto.es/bizkaisense/ucum-extended.owl#> . observation: rdf:type dc:date ssn:observedProperty ssn:observationResult ssn:observedBy observation:sensoroutput rdf:type

ssn:hasValue observation:outputvalue rdf:type dul:hasDataValue dul:isClassifiedBy ssn:Observation ; "2011-01-01T00:00:00" ; sweet:NO ; observation:sensoroutput ; bizkaisense:ELCIEG . ssn:SensorOutput ; observation:outputvalue . ssn:ObservationValue ; 3 ; ucum-extended:microgram-per-cubic-meter . 5

Conclusion and Future Work

On this paper we have described the e orts made to semantically annotate the Sensor Open Data provided by Open Data Euskadi. These e orts include the analysis of di erent ontologies from the domains of sensor networks, chemistry and meteorology; and the extension of these ontologies to ful l all the requirements of these pollution sensors. Even though the SWEET ontology belongs to a concrete domain, the extension of UCUM instances of MUO ontology can be reused in a wide variety of cross-domain projects. In addition, this semantic representation of pollution sensors allows us to make complex queries over their data, e. g. the queries used in Bizkaisense for calculating averages of certain substances in a region, as the one we can see in Code 2. Furthermore, this semantic model can be adopted by other sensor networks of the same domain. The approach of extending existing ontologies in contrast of creating new ad-hoc ontologies allows the interoperability among di erent sensor networks. On the other hand, this paper demonstrates the usefulness of Open Data platforms like ODE.

The next goal in Bizkaisense is the integration of data about pollution sensors with other data sources related to environmental domain, like solid and liquid wastes production of Basque Country. With the integration of more data sources we expect to appeal the experts of the domain to increase the features of the system and to demonstrate the real value of the Sensor Open Data vision.

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