=Paper=
{{Paper
|id=Vol-1936/paper-08
|storemode=property
|title=A Unified Semantic Ontology for Energy Management Applications
|pdfUrl=https://ceur-ws.org/Vol-1936/paper-08.pdf
|volume=Vol-1936
|authors=Javier Cuenca,Félix Larrinaga,Edward Curry
|dblpUrl=https://dblp.org/rec/conf/semweb/CuencaLC17
}}
==A Unified Semantic Ontology for Energy Management Applications==
https://ceur-ws.org/Vol-1936/paper-08.pdf
A Unified Semantic Ontology for Energy
Management Applications
Javier Cuenca1 , Felix Larrinaga1 and Edward Curry2
1
Mondragon University/Faculty of Engineering, Loramendi 4, 20500
Arrasate-Mondragon, Spain
jcuenca@mondragon.edu,flarrrinaga@mondragon.edu
2
Insight Centre for Data Analytics, National University of Ireland Galway, Ireland
ed.curry@deri.org
Abstract. Current research evidences an increase of use of Semantic
Web technologies within city energy management solutions. Different
ontologies have been developed in order to improve energy data interop-
erability. However, these ontologies represent different energy domains,
with different level of detail and using different terminology. This hetero-
geneity leads to an interoperability problem that hinders the full adop-
tion of these ontologies in real scenarios. This paper presents the OEMA
(Ontology for Energy Management Applications) ontology network. This
ontology is an attempt to unify existing heterogeneous ontologies that
represent energy performance and contextual data. The paper describes
the OEMA ontology network development process, which has included
ontology reuse, ontology engineering and ontology integration activities.
The paper also describes the main OEMA ontology network modules.
Keywords: Semantic Web, energy, ontology network, ontology integra-
tion
1 Introduction
Energy management in current cities is evolving towards the future Smart Grid.
Smart Grid managers aim to improve current grid efficiency, sustainability and
resilience through Information and Communication Technologies (ICT)-based
Energy Management Systems (EMSs). The efforts to improve energy efficiency
concentrate on enhancing EMS to optimize the use of renewable and non-renewable
energy sources. Energy sustainability measures include the suggestion of actions
to change energy management behavioural patterns for economic, social and eco-
logical purposes. Citizens are the main actors and the interoperability between
them and EMS is essential. Regarding resilience, the objective is to avoid and
react to power outages caused by power peak periods or natural disasters. Again,
human-machine collaboration is crucial.
Current EMSs mainly focus on improving energy efficiency. However, en-
ergy sustainability and resilience systems require new data representation and
exchange technologies [8]. Sustainability and resilience systems are required to
exchange, extract knowledge and make decisions from large volumes of energy
data collected at high rates and in most cases in real time. In addition, energy
data belong to many domains and includes energy performance data (i.e., energy
quantities, energy performance indicators, etc.) and energy-related and contex-
tual data (i.e., buildings/infrastructures data, geographical data, weather data,
etc.). ICT-based systems gather and store these data. Traditionally, these sys-
tems operate in functional silos and rely on heterogeneous technologies that pose
new interoperability challenges for new EMSs. These challenges include the cre-
ation of a common energy data representation model and common vocabularies
for human-machine interaction.
Current research in energy management shows the use of the Semantic Web
to overcome these challenges ([6], [11], [7]). From the beginning of the current
decade, Semantic Web technologies have been applied to create ontologies that
represent energy data for different domains. These ontologies are the knowledge
base of energy sustainability and resilience applications. However, not all ontolo-
gies represent the same energy data domains and at the same level of detail. In
addition, the ontologies use different vocabularies to describe the same energy
concepts. Hence, there is the need of an unified energy ontology that can be used
in a wide variety of Smart Grid scenarios (i.e., Smart Homes, microgrids, etc.).
This paper presents the OEMA (Ontology for Energy Management Appli-
cations) ontology network. This ontology network is an attempt to unify exist-
ing heterogeneous ontologies that represent different energy-related data. The
OEMA ontology network represents energy performance as well as contextual
data. The paper explains the methodology and main steps followed to develop
the OEMA ontology network. The paper also explains the OEMA ontology net-
work modularized structure. Finally, the paper discusses learnt lessons from the
OEMA ontology network development process and how future EMSs can benefit
from the ontology. The paper is organised as follows: Section 2 provides a state-
of-the-art of ontologies that represent energy domains. Section 3 emphasises on
the need of a standardized energy ontology. Section 4 explains the methodology
followed to develop the OEMA ontology network. Section 5 explains the main
components of the OEMA ontology network and the energy data it represents.
Finally, in Section 6 the conclusions of the research are presented.
2 Related Work
Within recent research projects and initiatives, semantic ontologies have been
proposed to represent energy related data used by different Smart Grid EMSs.
These systems are deployed in different Smart Grid scenarios: Smart Homes,
urban environments (i.e., building, district, city, etc.), organizations, microgrids
or Virtual Power Plants (VPPs) and Smart Grid Demand Response (DR) man-
agement.
The ThinkHome ontology [11] represents home energy consumption, pro-
duction and energy contextual data, i.e., building details, weather conditions,
etc. The DEFRAM project ontology [2] represents energy audits and measures
of industrial organizations and recommendations for improving energy manage-
ment given after previous audits. The SAREF4EE ontology [7] represents home
equipment, flexibility operations, home spaces and home environmental condi-
tions. The ontology BOnSAI [18] represents the following energy data: building
equipment and structure data, user location and energy and environmental con-
dition measures. This ontology is implemented within an EMS that monitors
building energy performance and shows this information to allow users taking
actions to increment energy savings.
The ontology EnergyUse 3 represents the following information: home user
profiles, home appliances, and Heating, Ventilation and Air Conditioning (HVAC)
systems data, home sensors and actuators data, home appliances energy con-
sumption measures and energy tips discussion data. This ontology is the base for
a collaborative web platform that is focused on raising home end users’ climate
change awareness [4]. The ProSGV3 ontology [9] represents the following data:
infrastructure data, electrical appliances data, energy generation and storage
systems data, weather report data, events, energy production and consumption
and information about energy producers and consumers. The final purpose is to
use this ontology as the knowledge base of EMSs focused on improving Smart
Grid DR and sustainability by predicting Smart Grid energy consumption and
production.
The Mirabel ontology [20] represents different energy actors’ (i.e., home end-
users) energy flexibility for specific devices. The DERI Linked Dataspace [6]
represents energy related data from different enterprise domains. These domains
include the following: enterprise business entities (i.e., employees, products, etc.),
enterprise infrastructures energy consumption, energy consumption measure-
ment sensors and business information, i.e., finance, facility management, etc.
This linked dataspace is used by an enterprise observatory system that is fo-
cused on improving enterprise energy management at different levels from both
economic and ecological perspectives.
The Km4city ontology 4 represents data domains about cities. These domains
include energy, mobility, statistics, street graph, sensors, cultural heritage, etc.
Represented energy domains include organisation and weather data. The SE-
MANCO ontology 5 represents the following energy domains: building energy
consumption data as well as associated energy performance indicators (i.e., en-
ergy costs), consumed energy sources, building features, building equipment,
weather conditions, buildings geographical location, demographic, environmen-
tal and socio-economic data. The objective of this ontology is to provide models
for urban energy systems to be able to assess the energy performance of an urban
area [5]. Finally, the LCC ontology [15] represents different buildings energy con-
sumption data. This ontology has been developed to publish energy consumption
data about cities’ infrastructures as Linked Data.
3
http://www.essepuntato.it/lode/http://socsem.open.ac.uk/ontologies/eu
4
http://www.essepuntato.it/lode/http://www.disit.org/km4city/schema
5
http://semanco-tools.eu/ontology-releases/eu/semanco/ontology/SEMANCO/
SEMANCO.owl?
3 The Need of a Unified Ontology
The energy ontologies reviewed in Section 2 represent different energy data do-
mains depending on the Smart Grid scenario where they are applied. Table 1
shows the level of detail with which some of the available reviewed energy ontolo-
gies represent the main energy domains. We consider that an ontology represents
an energy domain with a high level of detail when it includes a wide variety of
terms and complex class hierarchies about that domain. A medium level of detail
representation of an energy domain includes fewer terms and less complex class
hierarchies. Finally, a low level of detail representation includes only few classes
and virtually no class hierarchies.
As we can observe in Table 1, none of these ontologies represents all types of
energy performance and contextual data that should be taken into account for
energy management and the data are also represented at different levels of detail.
In addition, different terms are used to represent the same energy concepts.
ontology ThinkHome SAREF4EE BOnSAI ProSGV3
ontology ontology ontology ontology
energy domain [11] [18] [7] [9]
Infrastructure technical data H L M M
Energy consumption systems data H M L H
Energy performance data H H H H
Sensors/actuators data H M M M
Energy stakeholders’ data M - L L
Weather/climate data H L L M
Geographical data - - - L
Environmental data M - M -
Distributed energy sources data M L L M
Energy DR operations - M - L
Table 1: Energy domains representation level of detail
(H=High/M=Medium/L=Low)
This term and domain representation diversity, called semantic heterogeneity
[12], leads to an interoperability problem that hinders the full adoption of these
ontologies in real scenarios. Hence, there is the need of creating a unified ontol-
ogy that represents all energy domains providing a common terminology. This
ontology can be a standard knowledge base of EMSs applied in any Smart Grid
scenario. Moreover, a unified ontology would reduce the effort spent by energy
management developers when creating energy ontologies and enable them to be
more focused on application implementation.
An ontology can be developed as a whole or as a set of interconnected on-
tologies, what is known as an ontology network [19]. An ontology network allows
classifying energy data from different domains into domain ontologies that can
be linked by establishing top-level relations between energy concepts from dif-
ferent domains. Hence, this modularized approach would improve the ontology
reusability.
Considering all this, the authors have developed the OEMA ontology net-
work. It represents all identified energy domains in different existing energy
ontologies at a high level of detail. The OEMA ontology network provides a
common representation of concepts that belong to different energy domains.
The following sections explain the ontology development process and the ontol-
ogy structure.
4 Ontology Development Process
This section explains the OEMA ontology network development process with
phases for requirements definition, ontology selection for reuse, otology imple-
mentation and integration and ontology evaluation. The development process has
followed the steps and guidelines defined by [15] and [19]. [15] provides ontol-
ogy development steps and explains them through an application example that
corresponds to an energy ontology. [19] provides the so-called NeOn methodol-
ogy for developing ontologies and ontology networks. The following subsections
explain the different OEMA ontology network developing phases.
4.1 Ontology Requirements Definition
During this phase, the OEMA ontology network functional and non-functional
requirements have been defined. These requirements have been defined taking as
a reference requirements defined in [15] and guidelines proposed in [19]. As it is
developed to be used by different energy management applications, the OEMA
ontology network must represent all energy data domains that are shown in
Table 1 at a high level of detail. The domains include energy performance data
and energy-related contextual data. The next step has been the definition of the
top-level relations among these domains.
The OEMA ontology network non-functional requirements cover the follow-
ing aspects:
– Represented energy domains must be classified in different sub-ontologies or
modules, which are known as domain ontologies. This modularized structure
will ease ontology reuse and modification when adapting it to different energy
management scenarios/applications.
– One of the goals of the OEMA ontology network is to provide a common
representation of energy data. Hence, each ontology element (class, property)
must be named using only one term in order to avoid semantic heterogeneity.
– The Web Ontology Language (OWL-2 6 ) and ontology elements naming
notation (CamelCase) have also been defined.
6
http://www.w3.org/TR/2004/REC-owl-guide-20040210/
4.2 Ontology Selection for Reuse
The next step was the selection of existing energy ontologies and terms to be
reused during the OEMA ontology network development. The ontologies (include
the ones reviewed in Section 2) have been evaluated taking into account the
ontology requirements defined previously. The authors checked whether previous
ontologies represent the energy data domains shown in Table 1. The authors also
considered the level of detail when representing the energy domains (see Table
1).
The ThinkHome ontology is one of the ontologies that represents most en-
ergy domains with a high level of detail. Although it is designed to be used by
home energy management applications, the ontology can also be extended to
other Smart Grid scenarios, i.e., organisations energy management, microgrids
energy management, etc. In addition, the ThinkHome ontology classifies energy
concepts in different domain ontologies. Due to its completeness and its modu-
lar approach, the ThinkHome ontology has been selected to be the base for the
OEMA ontology network.
Other energy ontologies have also been selected for reuse in order to com-
plement OEMA. They have been used to represent missing concepts in specific
energy domains and to enrich the ThinkHome ontology. Additionally, authors
selected also DBpedia [1] and FOAF [3] ontologies for reuse. As an example,
Table 2 shows which ontologies and concepts have been selected by the authors
for reuse to represent the energy consumption systems domain.
These concepts have been selected semi-automatically with the help of the
AgreementMaker ontology mapper. AgreementMaker has helped to find the on-
tologies that represent the same concepts with different terms and class hier-
archies. It also has allowed identifying those classes and properties related to a
certain term that are represented by one ontology but not by others. Based on
the ontology mapper results, unique terms and class hierarchies have been chosen
to represent repeated concepts among different energy ontologies. The criteria
followed to choose terms and class hierarchies has been to select a target class
hierarchy that represents each energy concept in the most detail. Terms from
other energy ontologies are then selected in order to enrich the selected class
hierarchy. This criteria is considered an asymmetric ontology merging method
and is used to merge ontologies avoiding overlapping concepts [16].
4.3 Ontology Implementation
The next stage is the implementation/development of the ontology. During the
development process, the selected energy ontologies have been merged according
to selected concepts and class hierarchies in the previous step. The OEMA ontol-
ogy network development process has been performed in five iterations: ontology
structure definition, ontology reuse, adding new information to the ontology, on-
tology integration and ontology evaluation.
First, the OEMA ontology network base structure was defined. As detailed
in Section 4.2, the ThinkHome ontology is the base of the OEMA ontology net-
work. The ThinkHome domain ontologies have been restructured to adapt them
Energy domain Ontology Reused concepts
HVAC systems, communication appliances, entertainment
appliances, office automation devices, lighting systems,
ThinkHome
white goods, acoustic systems, domotic network components,
Energy ontology
energy facilities, device state, device commands,
consumption
equipment manufacturer, external and internal equipment
systems data
SAREF4EE Appliances working modes and power profiles,
ontology device manufacturer and device model.
EnergyUse
Wearable devices.
ontology
Body care devices, pressing devices, water heating devices,
ProSGV3
charging devices, lighting systems, entertainment devices,
ontology
cleaning devices and electrical appliance category.
Table 2: Reused ontologies and concepts
to other Smart Grid scenarios in addition to the Smart Home energy manage-
ment scenario. This restructuring process consisted of renaming and including
new super-classes for domain ontologies. For example, the ThinkHome building
ontology represents building physical elements, building internal and external
equipment, and building geometrical features. The owl:Infrastructure class has
been added to this ontology in order to represent infrastructures (i.e., microgrids)
in addition to homes and buildings. Then, all top-level classes of the ThinkHome
building ontology that refer to homes and buildings have been included as sub-
classes of owl:Infrastructure class. As a result, the OEMA infrastructure ontology
has been created. This process has been repeated in all of the ThinkHome ontolo-
gies and as a result the first OEMA domain ontologies have been created: OEMA
infrastructure ontology, OEMA Smart Grid stakeholders ontology, OEMA exter-
nal factors ontology, OEMA energy and equipment ontology. Energy concepts
represented by each of these ontologies are later explained in Section 5.
After defining the OEMA ontology network structure, the previously selected
energy concepts (see Table 2) and associated statements have been merged and
added to each OEMA domain ontology. During the ontology reuse process, the
following techniques have been applied:
1. Specialization: adding reused classes and properties of one ontology as sub-
classes and subproperties of another ontology. For example, infrastructure
types reused from other ontologies have been added as subclasses of the
OEMA infrastructure ontology owl:Infrastructure class.
2. Generalization: adding reused classes and properties of one ontology as
super-classes and super-properties of another ontology. For example, white
goods types (i.e., cleaning devices) reused from other ontologies have been
added as super-classes of cleaning and cooking white goods of OEMA energy
and equipment ontology.
In addition, the following ontology engineering activities have been per-
formed:
A Knowledge extension: creating new classes and properties for relating reused
concepts with concepts from different ontologies. For example, when reusing
infrastructure premises (i.e., residential premises, business premises, etc.) in
the OEMA infrastructure ontology, the owl:hasPremise property has been
created in order to relate the owl:Infrastructure class with reused infrastruc-
ture premises classes.
B Renaming ontology elements: renaming reused ontology classes or properties
by changing their identifiers. This change has been performed to unify all
reused ontology resources naming according to CamelCase notation.
C Changing property domains and ranges: adding new domains and ranges to
properties of reused ontologies. For example, the owl:contains-Building prop-
erty from OEMA infrastructure ontology had only the owl:Cam-pus class as
a domain. This property has been changed to have also the owl:Infrastructure
class as a domain. With this change the ontology asserts that other infras-
tructures apart from campuses (i.e., power stations) contain buildings.
D New ontologies creation: The DBpedia ontology includes many concepts
about geographical, persons and organisations data. Adding all these con-
cepts to any of the OEMA ontologies would hinder the ontology maintenance.
Hence, two new ontologies have been created: OEMA geographical ontology
and OEMA person and organisation ontology. In addition, some units of
measure (i.e., volume, currency, etc.) are linked with concepts represented
in different OEMA ontologies. Hence, a new ontology has also been created
in order to modularize this aspect: the OEMA units ontology.
E Knowledge relocation: moving knowledge from one ontology to another. The
purpose of this change is to group all concepts of a specific domain in one
ontology in order to improve the OEMA ontology network maintainability.
For example, population socio-economic factors have been moved from the
OEMA geographical ontology to OEMA external factors ontology.
F Ontology Design Patterns (ODP) application: the N-ary ODP [13] has been
reused to restructure reused concepts.
The next step was to add missing concepts not represented in the reviewed
ontologies that are used in different EMSs or are present in well-known standards
such as USEF and OpenADR [10]. Some of these concepts are: energy saving
tips, Electric Vehicle (EV), energy market roles, etc. This step has led to the
creation of a new domain ontology: the OEMA energy saving ontology. This
ontology adds infrastructures and equipment energy saving recommendations to
OEMA ontology network.
Finally, OEMA ontology network domain ontologies were integrated. The
OEMA ontology network .owl file has been created and all domain ontologies
have been imported in this file. After importing each ontology, the following
tasks have been performed:
1. Ontology linking: the top-level relations between different OEMA domain
ontologies have been stablished through new properties.
2. Duplication removal : duplicate concepts among domain ontologies have been
eliminated. Disjoint relations have been established between classes that use
the same terms to describe different concepts, i.e., operational device state
and country state.AgreementMaker has been used again in order to detect
duplicate concetps among domain ontologies.
4.4 Ontology Evaluation
Finally, the OEMA ontology network has been evaluated with the OOPS! Pitfall
Scanner [14], which detects common pitfalls made during the ontology develop-
ment process. According to OOPS! feedback, the OEMA ontologies pitfalls have
been corrected. The logical consistency of OEMA ontologies has been also eval-
uated with the Pellet reasoner [17].
5 The OEMA Ontology Network
In this section, the OEMA ontology network is described. The OEMA ontology
network is made up of eight interconnected domain ontologies. Each ontology
represents one or more energy domains. These ontologies are connected by a core
ontology. OEMA top-level structure is shown in Figure 1.
Fig. 1: OEMA ontology network structure
– OEMA infrastructure ontology 7 : contains data about Infrastructures/buildings.
These data include infrastructure/building types (i.e., household, microgrid,
etc.), technical data (i.e., material, surface, etc.), spaces data (i.e., floors,
rooms, etc.), geometrical data (i.e., floor area), etc.
7
www.purl.org/oema/infrastructure
– OEMA energy and equipment ontology 8 : represents energy equipment such
as building automation system resources (sensors, actuators/controllers and
HVAC systems), industrial equipment (i.e., construction and manufacturing
equipment), energy generators (i.e., EVs, Home Power Plants, etc.), loads
(white and brown goods), power storage/energy carriers, etc. The ontology
also represents the energy equipment features such as devices’ power curve
and power profile or device state.
– OEMA geographical ontology 9 : represents geographical data about infras-
tructures and energy equipment locations. These data includes populated
places (i.e., country, city, district, etc.), natural places (i.e., mountain, sea,
etc.) and places geographical attributes such as altitude, depth or area.
– OEMA external factors ontology 10 : captures external factors that can in-
fluence energy usage. These factors include climate type (i.e., alpine, conti-
nental, etc.), environmental conditions (i.e., lighting, noise, etc.) household
socio-economic factors (i.e., household income, housing price, etc.), people
socio-economic factors (i.e., salary, education level, etc.), population socio-
economic factors (i.e., density, main origin, etc.), and weather phenomenon
(i.e., temperature), etc.
– OEMA person and organisation ontology 11 : represents person and organisa-
tion data: person and person attributes (i.e., age, gender, etc.), organisation,
organisation internal structure (i.e., departments), organisations economic
data (i.e., endowment, net income, etc.), person roles in organisations (i.e.,
role in project, occupation), etc.
– OEMA energy saving ontology 12 : represents general and personalized energy
saving recommendations.
– OEMA Smart Grid stakeholders ontology 13 : represents Smart Grid stake-
holders and roles in the energy market (i.e., energy consumers, energy sup-
pliers, Distribution System Operators (DSOs), etc.) and energy flexibility
operations, (i.e. market processes, flex-offers exchange), etc.
– OEMA units ontology 14 : represents different units of measure used by the
OEMA domain ontologies. These units of measure include energy units, area
units, capacity units, currency, density units, etc. The OEMA units of mea-
surement ontology is reused by OEMA infrastructure, energy and equipment,
geographical, and external factors ontologies.
6 Conclusion and Outlook
This paper presents the OEMA (Ontology for Energy Management Applica-
tions) ontology network. This ontology network is an attempt to unify existing
8
www.purl.org/oema/enaeq
9
www.purl.org/oema/geographical
10
www.purl.org/oema/externalfactors
11
www.purl.org/oema/pao
12
www.purl.org/oema/energysaving
13
www.purl.org/oema/sgstakeholders
14
www.purl.org/oema/units
heterogeneous energy ontologies that represent energy performance and energy
contextual data.
The diversity of terminology and level of detail of energy domains represented
by reused energy ontologies have brought the main challenges of the OEMA on-
tology network development process. Hence, reused ontologies concepts selection
and ontology engineering activities for linking concepts of different energy on-
tologies have been the most complex and time-consuming tasks for the ontology
network development process. Energy consumption systems data and infrastruc-
ture data are energy domains that present more heterogeneity among existing
energy ontologies. Thus, the energy concepts selection task has been particularly
challenging when selecting terms that belong to these energy domains. Most
concepts and statements from existing energy ontologies have been copied into
the OEMA ontology network instead of importing them. Hence, changes that
reused ontologies may suffer in the future will not have an impact on the OEMA
ontology network. However, new concepts added to reused ontologies must be
analysed in order to include them or not in the OEMA ontology network.
The OEMA ontology network is made up of eight interconnected domain
ontologies. Each ontology represents one or more energy domains. These domains
include both energy performance and contextual data. The OEMA ontology
network will enable energy applications to extract knowledge and make decisions
about large volumes of energy data from different domains. In addition, the
modularized approach of the OEMA ontology network approach will facilitate
reuse and modification when adapting it to different Smart Grid scenarios and
energy management applications. The main contributions of the OEMA ontology
network are:
1. Common representation of energy domains: it represents in a unified manner
and at a high level of detail energy domains that are described by existing
energy ontologies using different vocabularies and varying levels of detail.
2. Integration of different energy domains: it represents all energy domains
captured in different energy ontologies in a single ontology network.
Considering these benefits, the OEMA ontology network provides a starting
point for a widely accepted energy ontology. Future work will focus on the OEMA
ontology network validation by implementing it in real Smart Grid scenarios, i.e.,
microgrid energy management, home energy management, etc.
References
1. Auer, S., Bizer, C., Kobilarov, G., Lehmann, J., Cyganiak, R., Ives, Z.: Dbpedia:
A nucleus for a web of open data. In: The semantic web, pp. 722–735. Springer
(2007)
2. Blomqvist, E., Thollanderb, P., Keskisärkkä, R., Para-
monovab, S.: Energy efficiency measures as linked open data.
Available: http://www.semantic-web-journal.net/content/
energy-efficiency-measures-linked-open-data
3. Brickley, D., Miller, L.: Foaf vocabulary specification 0.91 (2007)
4. Burel, G., Piccolo, L.S., Alani, H.: Energyuse-a collective semantic platform for
monitoring and discussing energy consumption. In: International Semantic Web
Conference. pp. 257–272. Springer (2016)
5. Corrado, V., Ballarini, I., Madrazo, L., Nemirovskij, G.: Data structuring for the
ontological modelling of urban energy systems: The experience of the semanco
project. Sustainable Cities and Society 14, 223–235 (2015)
6. Curry, E., Hasan, S., O’Riain, S.: Enterprise energy management using a linked
dataspace for energy intelligence. In: Sustainable Internet and ICT for Sustainabil-
ity (SustainIT), 2012. pp. 1–6. IEEE (2012)
7. Daniele, L., Solanki, M., den Hartog, F., Roes, J.: Interoperability for smart ap-
pliances in the iot world. In: International Semantic Web Conference. pp. 21–29.
Springer (2016)
8. Finger, M., Portmann, E.: What are cognitive cities? In: Towards Cognitive Cities,
pp. 1–11. Springer (2016)
9. Gillani, S., Laforest, F., Picard, G.: A generic ontology for prosumer-oriented smart
grid. In: EDBT/ICDT Workshops. pp. 134–139 (2014)
10. Hippolyte, J., Howell, S., Yuce, B., Mourshed, M., Sleiman, H., Vinyals, M., Van-
hee, L.: Ontology-based demand-side flexibility management in smart grids using a
multi-agent system. In: Smart Cities Conference (ISC2), 2016 IEEE International.
pp. 1–7. IEEE (2016)
11. Kofler, M.J., Reinisch, C., Kastner, W.: A semantic representation of energy-
related information in future smart homes. Energy and Buildings 47, 169–179
(2012)
12. Maree, M., Belkhatir, M.: Addressing semantic heterogeneity through multiple
knowledge base assisted merging of domain-specific ontologies. Knowledge-Based
Systems 73, 199–211 (2015)
13. Noy, N., Rector, A., Hayes, P., Welty, C.: Defining n-ary relations on the semantic
web. W3C working group note 12(4) (2006)
14. Poveda-Villalón, M., Gómez-Pérez, A., Suárez-Figueroa, M.C.: Oops!(ontology pit-
fall scanner!): An on-line tool for ontology evaluation. International Journal on
Semantic Web and Information Systems (IJSWIS) 10(2), 7–34 (2014)
15. Radulovic, F., Poveda-Villalón, M., Vila-Suero, D., Rodrı́guez-Doncel, V., Garcı́a-
Castro, R., Gómez-Pérez, A.: Guidelines for linked data generation and publication:
An example in building energy consumption. Automation in Construction 57, 178–
187 (2015)
16. Raunich, S., Rahm, E.: Towards a benchmark for ontology merging. In: OTM
Workshops. vol. 7567, pp. 124–133 (2012)
17. Sirin, E., Parsia, B., Grau, B.C., Kalyanpur, A., Katz, Y.: Pellet: A practical owl-
dl reasoner. Web Semantics: science, services and agents on the World Wide Web
5(2), 51–53 (2007)
18. Stavropoulos, T.G., Vrakas, D., Vlachava, D., Bassiliades, N.: Bonsai: a smart
building ontology for ambient intelligence. In: Proceedings of the 2nd international
conference on web intelligence, mining and semantics. p. 30. ACM (2012)
19. Suárez-Figueroa, M.C.: NeOn Methodology for building ontology networks: speci-
fication, scheduling and reuse. Ph.D. thesis, Informatica (2010)
20. Verhoosel, J., Rothengatter, D., Rumph, F., Konsman, M.: An ontology for mod-
eling flexibility in smart grid energy management. In: eWork and eBusiness in
Architecture, Engineering and Construction-European Conference on Product and
Process Modelling, ECPPM 2012, 25-27 July 2012, Reykjavik, Iceland, 931-938
(2012)