During the latest years, Smart City projects aimed at driving local governments towards the strong use of technologies to support a higher quality of urban spaces and a better o ering of public services. From an information viewpoint, this means enabling di erent categories of users, including citizens, Public Administration (PA), utility and energy providers, to access the large amounts of data in multiple, heterogeneous Smart City data sources, by adopting new tools and methods to take decisions that might improve city daily life. Aggregation of urban data according to multiple perspectives through the de nition of proper indicators enables urban data exploration at di erent granularity levels for distinct categories of users. Furthermore, Semantic Web technologies may be used to enable interoperability and improve data access. In this paper, we propose a Smart Living Ontology, to provide a semanticenriched representation of city indicators. On top of the ontology and users' characterisation, a Semantic Layer has been designed to enable personalised access to urban data.
Smart City projects aim at driving local governments towards the strong use of
technologies to support a higher quality of urban spaces and public services [1{3].
From an information viewpoint, di erent categories of users, including citizens,
Public Administration (PA), utility and energy providers, must explore the large
amounts of data from multiple, heterogeneous data sources, in order to take
decisions that might improve city daily life. In recent research, Semantic Web
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technologies have been proposed to develop ontology-enabled applications, such
as Smart Urban Cockpits and dashboards [
In this paper, we propose a semantics-enabled framework, which relies on the so-called Smart Living Ontology, to provide a semantic representation of Smart City indicators. The framework also includes the kinds of activities and users' categories for which indicators have been designed to share relevant information. A Semantic Layer, developed on top of the ontology, enables personalised exploration of urban data. The framework has been designed for decision makers, who need to have a view on heterogeneous urban data at di erent aggregation levels, ranging from energy consumption to garbage collection, pollution levels, citizens' safety. For example, the framework may allow building managers to monitor electrical consumption of administered buildings, by exploiting the indicators hierarchy in the ontology to distinguish electrical consumption according to di erent perspectives (e.g., consumption in common spaces, consumption of elevators), and to compare average values of consumption with other buildings at district or city level. Furthermore, the framework may enable citizens to make decisions about their activities by observing speci c indicators (e.g., to avoid sport activities when pollution levels overtake tolerance thresholds).
This work was performed in the context of the Brescia Smart Living (BSL)
Italian project1, which promotes a holistic view of the city, where di erent types
of data are explored to provide new services to both citizens and PA. The
framework has been already presented in [
This paper is organised as follows: in Section 2 we highlight the cutting-edge features of our approach compared to the literature; in Section 3, motivations are presented; Section 4 presents the Smart Living Ontology; Section 5 describes the Semantic Layer of the framework; in Section 6 we discuss implementation details and preliminary validation; nally, Section 7 closes the paper. 2
In literature, the adoption of ontologies in Smart City projects targets energy
management, where diagnostic models are built to discover energetic losses [
With respect to an OBDA perspective, our objective is to focus on data exploration by exploiting a semantic characterisation of Smart City indicators, also considering users' category and activities for which indicators have been
designed. The semantic modelling we pursue also reinforces the characteristics
of the BSL project, that if compared to other Smart City projects [1{3] provides
a wider spectrum of urban data. Approaches focused on Ontology-Based Data
Warehouses (OBDW) store analytical data, indicators, requirements and their
semantics [
As a motivating example, let's consider John, the manager of several buildings located in di erent districts of the Smart City. John monitors the electrical consumption of the buildings, in order to implement energy saving policies (e.g., introducing LED lamps in common areas or planning renovation work to increase the energy e ciency class of buildings). Challenging issues are related to the capability of enabling John to fruitfully exploit available information. In this paper, we address such issues as follows.
Semantic speci cation of city indicators. Indicators are commonly de ned to aggregate data according to several dimensions, for di erent categories of users (e.g., consumption-based indicator of electrical energy use). In our framework, the Smart Living Ontology is de ned to provide semantic speci cation of the indicators, that takes into account indicators scope, in terms of spatial and temporal constraints, their hierarchical organisation and target users.
Personalised data exploration. Given the variety of urban data that can be explored, the selection of proper indicators is personalised taking into account users' pro les, composed of user's category, activities and preferential indicators.
Indicators recommendation to support decision making. Indicators recommendation is provided in order to help users to take decisions in their daily life. For example, John is provided with suggestions about indicators on electrical consumption of the administered buildings. To this aim, indicators scope and hierarchy, as well as ltering based on activities for which the indicators have been designed, are exploited to better focus the exploration. 4
2 The TBox of the ontology can be found at https://tinyurl.com/onto-schema (a free Web Protege account is required)
qb: Dimension Property
Dimension timeE:Tnetmityporal schema: Place schema: Administrative
Area scDhiesmtPrailca:ctcoentainsschemPala:ccoentains schema: City
Constraint time:
Instant time:hasBeginning
time:hasEnd time:
Interval hasTimeGranularity boundTo hasSpatialCoverage qbP:rMoepaesrtuyre hasBSLService
URL ObjectProperty DatatypeProperty rdfs:subClassOf
Legend
Imported Concept
Literal domain, we rely on some foundation ontologies to cover a set of required pivotal concepts: (i) a geospatial mapping of the main structures of the city (e.g., buildings, streets, areas) and their topology; (ii) temporal entities; (iii) other high level concepts, that have been specialised to de ne the hierarchy of indicators and activities.
Indicators are speci ed as individuals of the Indicator concept or one of its sub-concepts in the indicators hierarchy. An indicator is further relatedTo a set of domain individuals (e.g., environment, safety, energy, mobility) to de ne the indicator scope, and a set of constraints. As shown in Figure 1, in the SLO a constraint can be either a dimension (time and space) or a user's category (e.g., building manager). Speci cally, an indicator can be boundTo a time interval (e.g., values of electrical consumption available for the year 2017), may have a time granularity (hasTimeGranularity relationship), may be de ned at city, street, district or more speci c levels, such as buildings (hasSpatialCoverage relationship). Finally, knowledge about an indicator can be useful to perform speci c activities, de ned as individuals of the concept Activity or its subconcepts (influencedBy property). An indicator is linked to a web-based service of the BSL Platform (hasBSLService property) to display the indicator values on the Smart City Dashboard, as explained in Section 6.
BSL users are pro led according to their category, their activities, the types of indicators explored by the user through the interactions with the framework. In the next section we detail how personalised exploration of indicators is performed based on the SLO, with the help of the motivating example. 5
Personalised data exploration for each user can be achieved by e ectively exploiting city indicators, properly selected according to the indicators domains, constraints and pro les of users. Figure 2 reports the steps for semantics-enabled data exploration and the I/O for each step.
available indicators request for indicators inputs outputs
BSL Platform Web services
domain-driven indicators selection
activity-based indicators refinement filtering based on user’s category candidate indicators refined candidate indicators
For example, let's consider again the user John in the motivating example, who is the manager of three buildings (namely Building 1, Building 2 and Building 3) located in two districts of the city. Since John is usually interested in monitoring buildings, during the registration to the BSL platform, he speci es the activity Monitoring in his pro le, jointly with his administered buildings, associating them to the districts they are located in.
Candidate indicators selection. In order to have an insight on the status of the
buildings, for instance to evaluate whether replacing standard lamps with less
energy-demanding LED ones, John issues a request to the framework. To
support John in the request formulation, without requiring a detailed knowledge of
ontology concepts and individuals, the framework enables him to specify a set
of keywords Kr = fenergy, consumptiong, processed according to techniques
aimed to match the keywords with ontology terms [
Literal ObjectProperty DatatypeProperty rdfs:subClassOf
Downtown district
San Polino district schemPala:ccoentains
schemPala:ccoentains schemPala:ccoentains Building 1
Building 2
Building 3 indicators dimensions exploits both the knowledge on the spatial coverage of indicators and the information stored in the user's pro le. Starting from indicators previously selected for the John's building, the containment relationship that relates John's buildings with districts is exploited. Therefore, John could compare his buildings against others having similar characteristics or using different lighting solutions; this may stimulate John to consider the replacement of energy consuming light bulbs with modern LED lamps in shared spaces. 6
RESTfulServices SOAPServices MQTTAgents
FieldIoT(smartmeters,nextgenerationgasmeters, hydronicvalves,wearabledevices,...) andotherdatasources
We considered two kinds of requests: (A) requests where the user speci ed
a set of keywords Kr in order to identify desired domains and indicators, and
the user's pro le does not contain any activity or preferential indicator; (B)
requests where the user presents a richer pro le (containing category, activities
and preferential indicators), but speci es keywords in Kr, that only correspond
to individuals of the Domain concept. We compared our ontology-based approach
against a keyword-based search, where semantic disambiguation techniques have
been applied to Kr [
In this paper, we described a semantics-enabled framework composed of: (i)
a so-called Smart Living Ontology, apt to provide a exible representation of
Smart City indicators; (ii) a Semantic Layer, to enable personalised exploration
of urban data for di erent categories of users. The framework has been already
presented in [