A study of existing ontologies in the io t domain

A study of existing Ontologies in the
IoT-domain
Garvita Bajaj a
, Rachit Agarwal b
, Pushpendra Singh a
,
Nikolaos Georgantas b
, Valerie Issarny b
aIndraprastha Institute of Information Technology, New Delhi, India
bInria, Paris, France
Abstract
Several domains have adopted the increasing use of IoT-based devices to collect sen-
sor data for generating abstractions and perceptions of the real world. This sensor
data is multi-modal and heterogeneous in nature. This heterogeneity induces in-
teroperability issues while developing cross-domain applications, thereby restricting
the possibility of reusing sensor data to develop new applications. As a solution to
this, semantic approaches have been proposed in the literature to tackle problems
related to interoperability of sensor data. Several ontologies have been proposed to
handle different aspects of IoT-based sensor data collection, ranging from discover-
ing the IoT sensors for data collection to applying reasoning on the collected sensor
data for drawing inferences. In this paper, we survey these existing semantic ontolo-
gies to provide an overview of the recent developments in this field. We highlight the
fundamental ontological concepts (e.g., sensor-capabilities and context-awareness)
required for an IoT-based application, and survey the existing ontologies which
include these concepts. Based on our study, we also identify the shortcomings of
currently available ontologies, which serves as a stepping stone to state the need for
a common unified ontology for the IoT domain.
Key words: IoT, Ontologies, interoperability, heterogeneity
∗ Corresponding author. Email: garvitab@iiitd.ac.in;
∗∗Corresponding author. Email: rachit.agarwal@inria.fr;
Email addresses: garvitab@iiitd.ac.in (Garvita Bajaj),
rachit.agarwal@inria.fr (Rachit Agarwal), psingh@iiitd.ac.in (Pushpendra
Singh), nikolaos.georgantas@inria.fr (Nikolaos Georgantas),
valerie.issarny@inria.fr (Valerie Issarny).
Preprint submitted to Elsevier July 4, 2017
arXiv:1707.00112v1[cs.AI]1Jul2017

1 Introduction
With the rapid adoption of the Internet of Things (IoT) technology in various
domains including health, transportation, and manufacturing, the number of
IoT devices in the world is expected to increase to 50 billion by the end
of 2020 1
. These IoT devices collect an enormous amount of data using the
sensors embedded in them. According to a Cisco report, the amount of annual
global data traffic will reach 10.4 ZB (zettabytes) by 2019 2
. It is interesting
to note here that this data will be multi-modal in nature comprising of various
formats including video streams, images, and strings.
Handling such large-scale heterogeneous data and processing it in real-time
will be a key factor towards building smart applications [1]. Semantic ap-
proaches - ontologies - have been used to solve these issues related to large-
scale heterogeneity. Ontologies are defined as a “well-founded mechanism for
the representation and exchange of structured information” [2]. Existing works
have proposed the use of unified ontologies to tackle issues of interoperability
and automation associated with heterogeneity of sensor data [3–5]. However,
multiple possible unifications developed by domain experts [6] pose several
challenges as every unified ontology proposes its self-defined taxonomy.
Figure 1 illustrates an example scenario where the use of several possible
unifications of ontologies causes issues. Let us consider two IoT platforms de-
ployed at different geographic locations for building two different applications
for smart-health and smart-building domains respectively. Accessing data from
both the platforms at another location (e.g., a remote server using data from
both the platforms for developing another application) will lead to the chal-
lenges described below.
Firstly, the integration of multi-modal sensor data from different sources (i.e.,
sensors belonging to the two platforms in this scenario) results in complexity
and variability in information exchange as multiple sources (i.e., IoT sensors)
corresponding to the same sensor data follow the nomenclature [2] defined by
different ontologies. For example, Cloud 1 may store a sensor’s location using a
tag loc (defined by ontology A); while Cloud 2 may use the tag location for
referencing sensor location (defined by ontology B). This difference in nomen-
clatures may lead to usability issues in developing cross-domain applications
as developers would require prior knowledge of every data source and its as-
sociated ontology before selecting one for use.
1 https://www.statista.com/statistics/471264/iot-number-of-
connected-devices-worldwide/
2 http://www.cisco.com/c/dam/en/us/solutions/collateral/service-
provider/global-cloud-index-gci/white-paper-c11-738085.pdf
2

Figure 1. Challenges may arise with the use of semantic approaches in IoT appli-
cations. The lack of a common semantics may lead to interoperability and design
issues.
Secondly, the heterogeneous data representation also poses challenges in the
development of cross-domain applications that rely on the same set of sen-
sors [4] available with different IoT platforms. For example, the temperature
sensor used in the smart-health application and the smart-building applica-
tion would yield two temperature values - body temperature and room tem-
perature respectively - that cannot be used interchangeably even if the same
nomenclature (e.g., temperature or Temp) is used to describe the sensor data.
Thirdly, heterogeneity also causes problems in the design, interaction, and in-
tegration of automated solutions based on sensor data [7]. For example, with
the growing complexity of building automation systems and available devices,
there is a need to develop automated design approaches for building systems.
Developing such automated approaches would require a comprehensive specifi-
cation of the hardware and software components to avoid heterogeneity issues,
which can be solved using a common unified ontology.
Defining one comprehensive unified ontology for the domain of IoT may be
challenging as there are more than 200 domain ontologies available [8]. For
most of the ontologies, there are certain concepts peculiar to the IoT applica-
3

tion domains while some concepts used are common to all the IoT platforms.
Consider, for example, the two platforms shown in Figure 1. Platform 1 will
require concepts from health domain while Platform 2 may need concepts from
energy and location domains to cater to application needs. On the other hand,
both these platforms will require some concepts to answer the basic queries
posed by a developer including the location of a sensor and capabilities of a
sensor among others. Concepts belonging to the former group (application-
specific) comprise the vertical silos of an IoT ontology, while those belonging
to the latter group comprise the horizontal silos as shown in Figure 2. In this
work, we limit our discussion to the unification of concepts pertaining to the
horizontal silos in the IoT domain.
In 2012, the W3C Semantic Sensor Networks Incubator Group (SSN-XG) 3
was formed to address the issues of heterogeneity in sensor networks. Although
a standard, SSN 4
still fails to address several aspects of the IoT data such
as real-time data collection issues, providing a taxonomy for measurement
units, context, quantity kinds (the phenomena sensed), and exposing sensors
to services, which compels developers to define new concepts in IoT domain
while developing novel applications. Several ontologies have been proposed to
overcome the shortcomings of SSN, and most of them are limited to certain
concepts owing to the vastness of the domain.
In this work, we identify these standard concepts by identifying the com-
petency questions - “what are the queries that experts will submit to a
knowledge base to find answers?” [9]. We identify these questions using the
4W1H methodology [10, 11]. The answers to the competency questions will
help us identify the core concepts for a unified IoT ontology. In this paper,
we survey the existing IoT and IoT-related ontologies proposed since 2012 5
to identify the concepts that could be used towards building the unified IoT
ontology. We aim to identify these concepts to solve the heterogeneity issue
arising because of several domain ontologies available in the literature. From
the concepts identified, we also aim to structure the existing literature which
can serve as a tool for ontology developers. To summarise, we identify the
main concepts in an IoT-based application using the 4W1H methodology and
classify the existing ontologies on the basis of these concepts. Our study aims
to highlight the research gaps in existing ontologies which will help identify
concepts required for a unified standard ontology in this domain.
3 https://www.w3.org/2005/Incubator/ssn/ssnx/ssn
4 A new version of SSN is available, however, it still does not address the issues
discussed.
5 For a detailed study on context-aware ontologies in the domain of IoT before
2012, we refer the readers to [12].
4

Figure 2. Distributing the concepts requirement of an IoT-based application into
horizontal and vertical elements
Roadmap
We begin with identifying the core concepts by defining the competency ques-
tions using the 4W1H methodology in Section 2. We identify a structure within
the proposed ontologies with a survey in Section 3. In Section 4, we discuss
the different approaches used to evaluate the ontologies in order to identify
a common ground for comparing existing ontologies with each other. We end
this paper with a discussion in Section 5 on the need of a common unified
ontology for achieving semantic interoperability among IoT applications.
2 Identifying Core Concepts
An application based on IoT technology (e.g., a standalone mobile application
or a cloud-based application using static sensors) consists of several compo-
nents. These components are used to interact with several different sensors
that are attached to a platform. These sensors may be statically positioned or
dynamically moving for collecting data across a geographical area of interest.
An ideal IoT ontology should describe the core concepts common to all IoT
applications (i.e., horizontal), and concepts that are specific to applications
(i.e., vertical) as shown in Figure 2. However, to limit the concepts in a uni-
fied ontology for IoT domain, we focus on including all the horizontal core
concepts in this study. To identify these fundamental concepts required in an
ontology, we must identify the competency questions - queries submitted by
experts to a knowledge base to find answers [9]. Previous works by Haller et
al. [13], Wang et al. [14], and Bermudez-Edo et al. [15, 16] have identified
some of these questions to list the following different core concepts for the IoT
domain: Augmented Entity, User, Device, Resource, and Service among
others. These studies ([13–16]), however, do not consider all the core concepts
which can be used by several applications to infer complex information in IoT
5

scenarios.
4W1H [10, 11] is a popular approach that is being used to describe the basics
of an event/situation. To define the comprehensive list of concepts for an IoT
ontology, we rely on the use of 4W1H methodology. For this, we must define
five variables - four Ws (What, Where, When, Who) and one H (How) to list
the competency questions required for identification of the core IoT concepts.
These variables and the corresponding competency questions are listed below
followed by a detailed explanation of the identified concepts:
• Who: Who will provide the information required to develop the IoT
application? The answer to this question requires concepts to identify
the sources which provide data for building an IoT-based application.
This source is the sensor which is embedded in a platform that can be
a part of a testbed. Thus, to answer the ‘who’ question, an IoT ontology
must include concepts for sensor, platform, and testbed.
• What: What are the conditions under which the source must collect
data? Should the data be collected under certain circumstances only?
Answering this question requires the IoT ontology to include concepts to
define the context of the data source (e.g., the mobility of the sensor,
the activity being performed, whether the measurement was taken by the
device automatically or was there some human intervention).
• Where: Where should the data come from? This refers to the location
of the data source which can be defined using geo-coordinates, building
names, landmarks, etc. The IoT ontology must incorporate the different
ways to locate the data source for allowing developers to identify the data
source.
• When: When should the data collection happen? Should the sensor col-
lect data at specified timestamps, should it be at a regular frequency, or
over a span of time? An IoT ontology should provide concepts to support
different formats for defining the time for data collection.
• How: Once the data has been collected by the relevant sensors, how
should it be exposed to the developer for building the IoT application?
There should be concepts to support services for providing developers
with the access to this sensor data.
These competency questions help us identify the requirements of the core
concepts in a unified IoT ontology. We list and explain these concepts in
detail below (the concepts are highlighted in bold):
(1) An IoT application is based on sensor data collected from various hetero-
geneous sensors. A sensor is defined as a “source that produces a value
representing a quality of a phenomenon” [17]. Note that here sensor data
refers to both raw data captured by the sensor and the metadata that
describes a sensor.
6

(2) These sensors need a power supply to operate. They may be battery-
powered or may be attached to another power source for energy. This
“identifiable entity to which a sensor is attached” is called a platform [17].
(3) For a wide-scale deployment of IoT solutions provided by these plat-
forms, a large-scale realistic testbed is required [18]. This testbed should
provide functionalities to support the different kinds of sensors required
(based on the phenomenon they sense), and should provide mechanisms
to communicate the sensor data to the applications. The testbeds may
also internally store the captured sensor data in their proprietary format
for performing local data analysis.
(4) Given a testbed infrastructure (e.g., SmartSantander 6
, IoT-Lab 7
, Am-
biciti 8
), an identifiable service to access this raw or processed data is
required. The service should support a combination of sensor data to get
better contextual information (assimilation), and removal of redundant
sensor data (filtering) from multiple data sources. The service should
allow the users to discover information from the environment based on
certain search criteria.
(5) Further, since most of the sensor data is location and time specific,
the location and time information may be used to provide a common
context (“information about a location, its environmental phenomena,
and the people, devices, objects, and software agents it contains” [19])
for modeling the services in IoT frameworks [20]).
Several domains have proposed ontologies defined by domain experts for these
concepts. In this work, we study some ontologies centered around these con-
cepts.
3 Survey
In this section, we present a survey of ontologies in the IoT-domain based on
the concepts identified above. These ontologies are prevalent in several ap-
plication areas including Building Management Systems (BMS) [21], indoor
navigation [22], and smart-homes [23]. Ye et al. [2] have proposed a classifica-
tion of ontologies based on (i) expressiveness as light-weight or heavy-weight;
or (ii) generality as generic, domain-specific, or application-specific. In this
paper, we use the latter classification based on the functionalities provided
by the ontology. Since our focus is on the entire IoT domain and not on a
specific application, we restrict our discussion to generic and domain specific
ontologies only.
6 http://www.smartsantander.eu/
7 https://www.iot-lab.info/
8 https://io.ambiciti.mobi
7

Sensor
Ontologies
Sensor
Discovery
[15, 21, 31, 33]Sensor Data
Description
[4, 24, 29]
Sensor
Capabilities
[15, 16, 25, 27]
Extensibility
[21, 33]
Data Access
& Sharing
[25, 32]
Figure 3. The different problem areas tackled by existing sensor ontologies
3.1 Sensor ontologies
Existing sensor ontologies proposed in the literature aim to solve heterogeneity
problems associated with the hardware, software, and the data management
aspect of sensors. These aspects are highlighted in Figure 3 where we clas-
sify the different sensor-based ontologies based on the problems they tackle.
Please note that ‘sensor data’ may refer to the raw sensor data generated by
sensing the phenomenon, or the metadata information used to describe the
sensor capabilities. Here, we use the term ‘sensor data’ to refer to the raw
data generated by sensors, and ‘sensor capabilities’ to describe the metadata
information (e.g., the accuracy of a sensor and coverage range of a sensor.)
associated with these sensors. In the following text, we briefly introduce the
different proposed ontologies based on the generic concepts defined by them.
3.1.1 Generic Ontologies
In 2012, W3C (World Wide Web Consortium) proposed a standard ontol-
ogy - SSN (Semantic Sensor Network) ontology [24] for describing the sensor
resources and the data collected through these sensors as observations.
SSN ontology aims to solve the heterogeneity problems associated with sensor
discovery and sensor data collection but has limited concepts to support the
spatial and temporal association of sensor data with the resources. Also, it
is difficult to describe the different sensor capabilities using SSN as there is
only one concept for sensor description. To overcome this problem, Xue
8

et al. [25] proposed an ontology with concepts for sensor types - normal or
advanced, and also sensor capabilities - static, or dynamic. They also intro-
duce concepts to deal with issues of sensor management and data sharing in
sensor networks. However, their ontology supports a small number of sensors
in terms of the phenomenon they can sense, and provides semantic support for
only a limited number of sensor features. Further, in the ontology, the concept
of location is only limited to building rooms and building floors. This limits the
usage of this ontology to indoor applications only. Gyrard et al. [4, 26] over-
come this limitation by providing M3 ontology - a comprehensive ontology
proposed as an extension to W3C’s SSN ontology to support the description of
sensors, observations, phenomena, their units, and domains, which also allows
for reasoning on sensor data using rules to infer contextual information.
Since the platform to which the sensors are attached can be mobile, issues with
dynamicity and sensor discovery must also be dealt with. Concepts for dealing
with these issues were introduced in OntoSensor [27]. The ontology extends
concepts from SensorML 9
, ISO-19115 10
, and SUMO [28], to allow concepts
for the identification of sensor categories, behavior, relationships, functional-
ities, and meta-data regarding sensor characteristics, performance, and reli-
ability. OntoSensor aims to support interoperability and ontology-based in-
ferences that require aspects of physical sensing to be incorporated in the
ontology definition. The ontology is heavy-weight, has high usage complex-
ity, and lacks the ability to describe sensor observations. This limitation
was solved by MyOntoSens [29] which provides a generic and exhaustive
ontology for describing sensor observations and capabilities to reason over
the collected data. MyOntoSens has been proposed for the domain of wireless
sensor networks (WSN) and borrows several concepts and relationships from
existing ontologies including OntoSensor [27], SSN [24], and QUDT [30] which
makes it applicable to the IoT domain also. MyOntoSens provides concepts
to support sensor discovery and sensor registration with the platform.
Hirmer et al. [31] propose an ontology to support dynamic registration
and bindings of new sensors to a platform. They borrow the concepts of
Sensors, Things and their associated properties from SensorML, and intro-
duce an additional concept of ‘Adapter’ associated with every sensor. The
borrowed concepts support sensor discovery while the newly introduced con-
cepts provide/compute additional information about sensor data. For example,
adapters are used to compute the average quality of sensor data values from
the quality of the sensor provided by the manufacturer and the staleness of
the values generated by the sensor. This additional information is then used
to build a repository of the different possible sensor bindings - bindings of
different Sensors with different Adapters - which is then used to automate
9 https://en.wikipedia.org/wiki/SensorML
10 http://bit.ly/2nedvM1
9

the registration and binding tasks.
Shi et al. [32] identified the problems associated with inconsistencies in con-
cept definitions among existing ontologies and proposed a framework to over-
come them. Their ontology, Sensor Core Ontology (SCO), borrows concepts
from existing sensor ontologies with provision to add new concepts, thereby
supporting extensibility. The ontology focused mainly on concepts related to
sensor data where each sensor observation is associated with time, space (lo-
cation), and a theme (phenomenon being sensed). However, the description of
these concepts themselves is not detailed enough even though the focus is on
aspects of sensor data collection.
3.1.2 Domain-specific ontologies
In this section, we present the domain specific ontologies and limit our discus-
sion to domains including smart appliances, energy, and building management
systems which also form an integral part of the IoT ecosystem. Note that this
is not the comprehensive list of all the domains. There are 17 different do-
mains that have been identified 11
, but we focus only on the above-mentioned
domains. SAREF [33] (Smart Appliance REFerence) ontology exists in the
domain of smart appliances and aims to reuse and align concepts and relation-
ships in existing appliance-based ontologies. The concept of functions - one or
more commands supported by devices (sensors) defined in SAREF - supports
modularity and extensibility of the ontology, and also helps in maintenance of
the appliances. An extension of SAREF - SAREF4EE [34] has been proposed
to support interoperability with EEBus 12
and Energy@Home 13
standards.
Along similar lines, Dey et al. [35] propose an extension of OntoSensor on-
tology in the energy domain to include the spatial and temporal concepts of
sensor data. Another ontology which focuses on energy efficiency at building
level - Brick [21], has been proposed in the domain of Building Management
Systems (BMS). Brick proposes the use of tags and tagsets to specify sen-
sors and building subsystems, thus, enabling sensor discovery. The use of tags,
tagsets, and functional blocks provides support for extensibility and flexibility
for developing cross-building BMS applications. They do not cover the entire
range of sensors available in the market and limit their focus to sensors used
in smart-building applications. Since the concept of location used by them
is limited to ‘zones’ (HVAC zones, rooms, and floors) inside the buildings, it
can’t be extended to other application areas.
11 http://sensormeasurement.appspot.com/?p=ontologies
12 https://www.eebus.org/en/about-us/
13 http://www.energy-home.it/SitePages/Home.aspx
10

3.2 Context-Aware ontologies
As defined by Chen et al. [19], contexts are “used to describe places, agents,
and events”. Contexts can be classified as external or internal; and physical or
logical [36]. Several ontologies have been proposed for context-aware systems
to effectively label contextual information collected from sensor devices in the
form of sensor data. The following section reviews some of the generic and
domain specific context ontologies. We emphasize more on domain specific
ontologies as the context inferred in context-aware applications (including IoT-
based applications) is highly dependent on the domain in consideration. As
an example, for labeling a user activity, we need a complete understanding of
the possible activities in the domain as activities performed on a university
campus will be very different than the activities performed in a smart-home
environment. Thus, better concepts for semantic labeling of contexts can be
identified with domain-specific ontologies.
In IoT applications, contexts are often important to correctly interpret sen-
sor data [12]. Thus, as the first step towards semantic labeling of contexts
in IoT applications, Baldauf et al. [36] propose the common architecture
principles of context-aware systems based on the classification of contexts: ex-
ternal (physical) or internal (logical). External or physical contexts are those
that can be measured using physical sensors, while internal or logical contexts
are those that are explicitly specified by users or captured by monitoring
user interactions (e.g., a user’s goal or emotional state). The authors also
derive a layered conceptual design framework to explain the elements com-
mon to most context-aware architectures. To further describe the concepts for
contexts (places, agents, and events), Chen et al. [19] propose COBRA-
ONT for smart spaces. They describe places using <lat, lon, string-name>
with two kinds of places with different constraints. There are also agents such
as human and software, which are located in places and play certain roles to
perform some activities. An important contribution is the broker architec-
ture proposed by the authors, which can be used to acquire and reason over
contextual information from mobile devices in order to reduce the burden of
developers.
Since in IoT applications, the concept of a location may vary from a point to
a place of interest, COBRA-ONT is one of the most promising ontologies to
represent the location context of “things”. It not only can describe a point but
also be used to specify a place using a ‘string’ value for a location. To further
correlate contextual information with location, Kim et al. [37] propose an
ontology based on the information provided by mobile device sensors, both -
11

physical (e.g. WiFi, Bluetooth, etc.) and virtual (e.g., user schedule, web-logs,
etc.) to support context-aware services. The proposed ontology defines the
relations between different user locations and the contexts identified. The au-
thors further propose a reasoner upon this ontology and evaluate it to identify
locations. The result shows that their reasoner has higher location accuracy
than the GPS locations.
3.2.2 Domain-specific ontologies
Several domain-specific ontologies have been proposed for defining contextual
information as the contexts inferred from sensor data may vary largely based
on the domain. For example, in a university domain, the activities may be
limited to only a small subset such as reading, playing, sleeping, etc., while in a
smart-home, a large set of activities (e.g., cooking, cleaning, reading, sleeping,
etc.) may be encompassed. In this section, we will limit our study to context-
aware ontologies in the domain of smart-homes and activity recognition.
Smart-home technologies are now being developed to assist disabled and el-
derly individuals with dignified living. Okeyo et al. [38] propose an ontology
to semantically label the Activities of Daily Living (ADL) such as cook-
ing food, brushing teeth, etc. for the smart-homes domain. Their ontology
is based on dynamic segmentation of sensor data for variable time windows
to identify simple user activities. These simple activities are then used to in-
fer more complex activities. A limitation of their ontology is that it focuses
only on individual activities, and does not consider social activities (e.g., tea
party, business meetings, etc.) that might occur in a smart-home environment.
To accommodate these concepts, Bae et al. [23] present RADL (Recognizing
Activities of Daily Living) system to classify three different kinds of services
in smart-home environments. The ontology supports the discovery of devices
and their location in smart-home domains using concepts like Person, Sensor,
Device, Location, and AmIApplication. Reasoning on this ontology helps the
system discover the user activities such as individual and social.
Further, extending our study to activity recognition in the domain of a univer-
sity, Lee et al. [39] propose UAO (University Activity Ontology) which caters
to activities specific to a university campus (e.g.: attending a lecture or having
lunch in the cafeteria). The authors use concepts defined in CONON (CON-
text ONtology) [40] and introduce new concepts that are specific to activities
on a university campus. They propose a hierarchical division of concepts using
sub-ontologies to differentiate existing concepts from the new concepts. The
concepts borrowed from existing ontologies are placed in the upper hierar-
chical structure, while the newly introduced concepts occupy positions in the
lower hierarchical structure.
12

3.3 Location-based ontologies
Location is used to describe the spatial context (partly, physical context) of
users/devices. Although a subset of context, we consider location ontologies
separately as they have been used in several different areas like defining cul-
tural heritage [41], urban planning [42], etc., beyond IoT technologies. How-
ever, we limit our discussion to location ontologies defined in the domain of
IoT only.
WGS84 ontology [43] describes abstract concepts for defining SpatialThings
such as buildings, people, etc., and TemporalThings such as events, or time
durations. It also describes the geographical locations of these ‘things’ by
using concepts for defining the geo-coordinates using latitude, longitude,
and altitude. Concepts defined in this ontology are inherited from abstract
concepts for defining subclasses specific to the system. A more descriptive
location ontology is provided by Flury et al.[20] for context-aware services
as they identify the location to be a common denominator for modeling ser-
vices in context-aware environments. They provide a generic ontology for the
location concept for “device-based services encountered in ubiquitous comput-
ing environments”. They provide abstract mathematical models to categorize
the different location solutions (like Cartesian coordinates and numerical esti-
mation techniques) used to define location information. The different models
considered are as follows: (i) Geometric models (comprising of Cartesian co-
ordinates); (ii) Set-theoretic models (for defining location as an element of a
set, e.g., Cellular location, WiFi AP location, etc.); (iii) Graph-based models
(for defining locations in physically grounded networks, social networks, etc.),
and (iv) Semantic models (for defining locations defined using human-friendly
notations). However, in most IoT-based applications, location data is often
collected using sensor data. Kim et al. [44] rely on the use of sensor data
collected from mobile devices of users to estimate device location. They pro-
pose a reasoner built upon sensor data and information from location sensors
to estimate the location of users.
3.3.2 Domain-specific Ontologies
In this section, we restrict our discussion to location ontologies for indoor
navigation.
iLoc [22] is an ontology specified in the domain of indoor building navigation
and follows some of the best practices for defining a new ontology. It uses
13

concepts borrowed from several existing ontologies - QUDT 14
, W3C Geo vo-
cabulary, and vCard 15
and also supports extensibility. Since the root concept
Location is borrowed from W3C Geo vocabulary, iLoc can also be extended
to provide navigation for outdoor locations. The authors, however, do not
provide an extensive evaluation of the ontology, and only demonstrate the
usability through a use-case scenario.
3.4 Time-based ontologies
Time is used to describe the temporal context. Most of the IoT-related on-
tologies reuse existing ontologies that define the temporal context.
Many ontologies that define temporal context have been proposed in the
literature, for example, DAML-Time 16
(DARPA Agent Markup Language
project Time initiative), DAML-S 17
(DAML for Web Services), KSL-Time 18
(Stanford Knowledge Systems Lab Time ontology [45]) and OWL-Time on-
tology 19
to name a few. DAML-Time focused on concepts to provide a com-
mon understanding of time. DAML-S however, provides temporal concepts
required to define a web service such as profile, process and time. We refrain
ourselves from describing them in detail. KSL-Time ontology, on the other
hand, provides concepts to distinguish between different types of intervals
and granularity.
The most commonly used ontology is the OWL-Time ontology proposed by
Hobbs and Pan [46] that focuses on describing date-time information spec-
ified in Gregorian calendar format. Several updates to the initial version of
OWL-Time ontology have been proposed and implemented. The latest version
of OWL-Time ontology 20
provides more concepts while deprecating some.
In general, the OWL-Time ontology provides concepts to describe time us-
ing five main core concepts, namely, TemporalEntity, Instant, Interval,
ProperInterval, and DateTimeInterval. M. Grüninger in [47] verified the
older version of the OWL-Time ontology in an independent study. A light-
weight version of the OWL-Time ontology called Time-Entity is developed
14 http://www.qudt.org/
15 https://www.w3.org/TR/vcard-rdf/
16 http://www.cs.rochester.edu/~ferguson/daml/
17 http://www.daml.org/services/daml-s/0.9/
18 http://www.ksl.stanford.edu/ontologies/time
19 http://www.w3.org/2006/time#
20 http://w3c.github.io/sdw/time/
14

for applications needing only limited concepts [48].
Timeline ontology 21
is yet another time-based ontology. It extends Time On-
tology by providing various concepts such as ContinuousTimeLine, DiscreteTimeLine,
OriginMap, etc. Fikes et al. in [45] considered granularity of time to provide
more flexibility to the annotations. They considered both open and closed
intervals to include flexibility on top of temporal aspects such as Time-Point.
3.4.2 Domain-specific Ontologies
TimeML, a time Domain-Specific Language (DSL), has been proposed by
Pustejovsky et al. [49] to provide the ability to query specific data based
on duration, events, granularity and instant. An instance of TimeML is de-
fined in XML format. However, Pustejovsky et al. used TimeML to annotate
events within a Natural Language Processing system. Dey et al. [50] ex-
tended the use of TimeML to IoT systems and as a use-case, demonstrated
the usage of TimeML to enrich annotations for the Energy sensors. Zhang
et al. in [51] created a time ontology that is capable of representing temporal
aspects: (1) characterized by events either cultural or historical; and (2) in
Chinese calendar instead of Gregorian calendar.
4 Ontology Evaluation
The goal of an ontology is to provide a semantic framework which can solve
the problem of heterogeneity and interoperability associated with domain ap-
plications. This requires available ontologies to be comprehensive, readable,
extensible, scalable, and reusable. Several methods have been proposed to eval-
uate an ontology on these different aspects. As a first step, validation tools
like OntoCheck [52] and SAOPY 22
have been developed. These validation
tools help ontology creators to identify inconsistencies in naming conventions
and metadata completeness for (e.g., cardinality checks, class hierarchy, etc.).
Another tool called OOPS 23
was developed to identify pitfalls in the ontol-
ogy [53] based on 41 evaluation criteria 24
such as missing annotations, missing
domain or range, and identifying anomalies in the relationships. Vapour 25
is
yet another validation tool that focuses on identifying if the ontology is cor-
rectly published or not based on the best practices recipes 26
. RDF Triple
21 http://motools.sourceforge.net/timeline/timeline.html#term_Instant
22 http://iot.ee.surrey.ac.uk/citypulse/ontologies/sao/saopy.html
23 http://oops.linkeddata.es
24 http://oops.linkeddata.es/catalogue.jsp
25 http://linkeddata.uriburner.com:8000/vapour
26 https://www.w3.org/TR/swbp-vocab-pub/
15

Checker 27
is another tool that validates triples by finding typos and match-
ing namespaces for the used concepts. Bae et al. [23] have also proposed a
schema-based approach for evaluating the design of ontologies on their rich-
ness, depth, width, and inheritance. Note that there are other validation tools
also available 28
, but we restrict ourselves from comparing them with each
other.
Once these basic checks have been performed, ontologies must be evaluated on
parameters like scalability and extensibility. Bermudez et al. [15, 16] evalu-
ate IoT-lite on scalability and complexity using Round Trip Time (RTT) as a
measure. They annotate sensor data using their ontology and execute SPARQL
queries on annotated datasets containing a different number of triples. For
evaluating ontologies on extensibility, Shi et al. [32] propose a metric for ob-
jectively assessing the nomenclature of concepts used in the ontology based
on translatability, clarity, comprehensiveness, and popularity.
A survey by Brank et al. [54] highlights the following four major approaches
to evaluate an ontology: (1) by comparing it with another ontology in the same
domain; (2) by comparing the concepts identified with a set of documents con-
taining information about the domain in consideration; (3) by proposing an
application using the ontology to justify its usage; and (4) by asking domain
experts to evaluate it manually. Summarizing our study, we observe that no
single metric or approach can be used to evaluate an ontology. This has also
been identified by the authors in [55] where they identify eight different pa-
rameters to assess the quality of an ontology. These are accuracy, adaptabil-
ity, clarity, completeness, computational efficiency, conciseness, consistency,
and organizational fitness. They also develop a framework called Semantic
MetaWiki (SMW) to evaluate ontologies on these parameters. They identified
that no single parameter could be used to compare ontologies; the goodness
of an ontology depends on the application in consideration, and it cannot be
judged by a single metric.
Most of the works referenced in this survey have demonstrated the feasibility
of their ontologies using sample applications which are only a measure of the
usability of the ontology. For evaluating ontologies on other parameters, there
are independent measures but no single comprehensive metric for concrete
assessment.
27 http://graphite.ecs.soton.ac.uk/checker/
28 A comprehensive list of existing semantic validators can be found on https:
//www.w3.org/2001/sw/wiki/SWValidators
16

5 Discussion
Often systems propose proprietary ontologies for achieving semantic consis-
tency in different modules. However, ontologies proposed by one system can
often be used by other systems to improve interoperability. For example, au-
thors in [56] use W3C’s SSN ontology and propose a system to provide seman-
tic interoperability between the different protocols used by different classes of
IoT applications such as wearable devices, and hardware platforms like Rasp-
berry Pi, Arduino, etc. Another example of such a system is STAR-CITY
(Semantic Traffic Analysis and Reasoning for CITY) [57]. This system uses
multiple domain ontologies 29
to combine heterogeneous data sources - static
and dynamic, generating data that has variety, has high sampling rate, and
volume. The system provides traffic analysis and reasoning for efficient urban
planning which supports re-usability of heterogeneous, real-time data, and
expose them to the end users through REST-ful web-APIs by reusing and
combining existing domain ontologies instead of proposing a new one.
Another approach to reuse existing concepts in ontologies, while adding new
concepts specific to the domain, is to provide a hierarchical structure composed
of two sub-ontologies as suggested by Lee et al. [39]. The authors use existing
concepts as part of the ‘upper’ ontology and define new domain-specific con-
cepts as part of the ‘lower’ ontology in the hierarchy of sub-ontologies. This
hierarchical division enhances the readability, and thus improves the scope of
reusability of the proposed ontology.
Several works have now started reusing existing ontologies by combining them
to propose new ontologies for the IoT domain. These new ontologies are meant
for specific platforms aimed at collecting and integrating IoT data. However,
they lack one or more concepts identified in Section 2, rendering them incom-
plete for the IoT domain. IoT-Lite [15], for example, provides a lightweight
instantiation of the SSN ontology and extends it using SAO. They also include
dynamic semantics by introducing mathematical formulas to estimate missing
sensor values during the data annotation phase itself. This saves time as the
data is no longer required to be sent to a server for extrapolating missing sensor
values, rendering IoT-Lite dynamic, fast, and interoperable; thus, making it
suitable for constrained IoT environments. The concepts covered by IoT-Lite
include sensor information and location. VITAL ontology [58] also follows
a similar approach by combining concepts from SSN, QUDT, OWL-Time,
and WGS84 to define sensors, measurements, time, and location concepts re-
spectively. OpenIoT ontology [59] also uses SSN as a base to build upon
concepts required for IoT applications and testbeds - Observation, Sensor,
and Location. It further extends to define utility metrics for Service level
29 http://goo.gl/5TbTT2
17

agreements between its users and provided OpenIoT services.
Authors of IoT-O ontology [60] claimed to built IoT-O ontology such that it
is modular and focused on two sets of requirements - Conceptual and Func-
tional. They defined “conceptual” requirements as those requirements that
form the core of any IoT-related ontology. These were based on the descrip-
tion of devices, data, services and their lifecycle. Further, “functional” require-
ments were defined as those requirements that follow best practices defined
by the semantic community. IoT-O is one of the first approaches towards uni-
fication of the IoT ontologies. IoT-O reuses concepts from SSN, SAN, DUL,
QUDT, oneM2M and further, defines new concepts. Nevertheless, the ontol-
ogy lacks a complete/holistic view of context. FIESTA-IoT ontology [61] is
yet another attempt to unify existing ontologies for the IoT domain. It covers
most of the concepts identified in Section 2. However, it lacks concepts for
annotating context information such as place of interest, has no support for
context-aware services and has limited notion for activity, actuators, virtual
entities, etc. oneM2M ontology [62], on the other hand, although supported
by standardization bodies in IoT, also lacks contextual information. oneM2M
provides minimal concepts needed for representing a device and its function-
ality. oneM2M does not reuse any existing ontology to define the concepts.
Another ontology called Open-MultiNet (ONM) [63] is built to define fed-
erations of infrastructures. Similar to oneM2M, ONM does not reuse concepts.
Further, ONM also lacks the notion of context.
An early attempt by Hachem et al. [64] to solve the challenges of hetero-
geneity and scalability in IoT, proposed three ontologies for a service-oriented
middleware for IoT applications. They are - (i) Device ontology (for describing
the physical things and their metadata), (ii) Domain ontology (for defining re-
lations between physical objects and mathematical formulas/functions), and
(iii) Estimation ontology (for describing automated estimation processes to
relate one mathematical function with another, and to model the errors in
estimations). These ontologies lack the concepts required to annotate the col-
lected data for addressing the issues of heterogeneity and interoperability. A
detailed model to define concepts for devices, services, context, and informa-
tion is also provided by IoT-A ontology [65] for the IoT Architecture Refer-
ence Model [66]. Note that, as we have described earlier, all these ontologies
are designed to satisfy specific requirements for the platform they are applied
in and thus, do not completely follow the requirements set in Section 2.
Even with so many ontologies defined for IoT-specific platforms, a single com-
prehensive ontology comprising of the core concepts is missing. Developers
should, therefore, reuse existing ontologies and merge them into new ontolo-
gies as per their requirements. Before defining a new ontology, they should
identify the competency questions. If the competency questions can be an-
swered by existing ontologies, then developers should emphasize on reusing
18

concepts and relationships from existing ontologies. This allows easy integra-
tion of heterogeneous data and facilitates developing reasoning capabilities
on top of semantic data. The reasoners can provide useful insights about the
systems and their output/reasoning data can also serve as raw data for other
systems/applications. However, if a developer has to define new concepts for
his/her systems, he/she should follow the best practices. These best practices
are: reusing existing concepts as much as possible and aligning the concepts
with equivalence relations [67], validating the ontology using ontology val-
idators (see Section 4), having recommended ontology metadata 30
, making
the ontology accessible, following a modular approach so that the ontology is
reusable, and documenting it with samples to demonstrate its usage [68].
6 Conclusion
In this paper, we study existing ontologies in the domain of IoT and identify
the major concepts listed by them. We identify that no existing ontology is
comprehensive enough to document all the concepts required for semantically
annotating an end-to-end IoT application as ontologies are often restricted to
a certain domain. Hence, as a step towards developing a comprehensive unified
ontology for IoT domain, we identify the core concepts required for develop-
ing an IoT application. We study the ontologies proposed for those concepts
and analyse the methods used for their evaluation. Our study identifies that
there are no concrete methods for evaluating an ontology, and developers must
always follow the best practices while publishing a new ontology in order to
enhance readability, usability, extensibility, and interoperability.
Acknowledgement
This work is partially funded by the European project “Federated Interoper-
able Semantic IoT/cloud Testbeds and Applications (FIESTA-IoT)” from the
European Union’s Horizon 2020 Programme with the Grant Agreement No.
CNECT-ICT-643943.
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