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Angelos Chatzimichail, Evangelos A.
Stathopoulos, Dimos Ntioudis, Athina
Tsanousa, Maria Rousi, Athanasios
Mavropoulos, Georgios Meditskos, Stefanos
Vrochidis, Ioannis Kompatsiaris
IoT and Semantic Web
Technologies
November 12, 2020
Springer Nature
Contents
1 Semantic Web and IoT .......................................... 1
1.1 Introduction ............................................... 1
1.2 Applications, Related Work and Research Challenges in Semantic
WebandIoT............................................... 2
1.2.1 Applications on different domains . . . . . . . . . . . . . . . . . . . . . . 3
1.2.2 Main Research challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
1.3 IoT Knowledge Representation with Semantic Web Technologies . . . 11
1.3.1 Modelling sensors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
1.3.2 Modelling multi-modal events and observations . . . . . . . . . . . 13
1.3.3 Ontology-Based Reasoning . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
1.4 The Semantic Web of Things and how it augments the IoT . . . . . . . . 23
1.5 Conclusion................................................ 24
References ..................................................... 24
v
Chapter 1
Semantic Web and IoT
Abstract In this chapter, we provide an overview of the current trends in using seman-
tic technologies in the IoT domain, presenting practical applications and use cases
in different domains, such as in the healthcare domain (home care and occupational
health), disaster management, public events, precision agriculture, intelligent trans-
portation, building and infrastructure management. More specifically, we elaborate
on semantic web-enabled middleware, frameworks and architectures (e.g. seman-
tic descriptors for M2M) proposed to overcome the limitations of device and data
heterogeneity. We present recent advances in structuring, modelling (e.g. RDFa,
JSON-LD) and semantically enriching data and information derived from sensor
environments, focusing on the advanced conceptual modelling capabilities offered
by semantic web ontology languages (e.g. RDF/OWL2). Querying and validation
solutions on top of RDF graphs and Linked Data (e.g. SPARQL, SPIN and SHACL)
are also presented. Furthermore, insights are provided on reasoning, aggregation,
fusion and interpretation solutions that aim to intelligently process and ingest sensor
information, infusing also human awareness for advanced situational awareness.
1.1 Introduction
The era of the Internet of Things (IoT) is upon us, with a huge number of IoT de-
vices already in everybody lives. A large number of applications in Smart Cities,
E-health, Security and other domains are exploiting the IoT technologies, like sen-
sors, smartphones and actuators. One of the most significant aspects of IoT is the
things interconnection providing an interconnected system of different services and
applications. Everyday huge amounts of data are being generated and these data pro-
vide extremely valuable knowledge databases. The IoT tries to estimate a situation
based on the data knowledge in order to enable services to make smart decisions.
However, several challenges arose with the existing IoT technologies regarding
the interoperability of those different IoT technologies since their data is based on
predetermined formats without following a catholic vocabulary to describe the in-
1
2 1 Semantic Web and IoT
teroperable data. The basic structure of the IoT is the Machine-to-Machine (M2M)
communication. For example, the measurements of sensors are required to be dis-
tributed and analyzed by other devices or sensors and not being human readable
without any kind of processing. Threfore, those measurements should be under-
standable from one machine to another. Although, towards the rendering capable of
global machine communication by autonomous information discovery and analysis,
it is mandatory to struct and group data respectively.
Semantic Web (SW) technologies have been widely used to interpret and integrate
data deriving from a plethoric variety of resources on the Web. The main objective
of the Semantic Web is the provision of a new form of content that is understand-
able and can be edited by both humans and computers. IoT domain has recently
adopted several Semantic Web technologies in order to enhance the content of data
and interoperability. This is achieved by virtualization IoT data based on reusable
vocabularies that can be interpreted by distinct software modules. This semantic an-
notation employs various semantic web standards, such as RDF, RDFs and OWL to
construct intellectual models, like ontologies to describe different domain concepts
and the connections that exist among them. Through this way, the semantic anno-
tation transforms the human oriented Web to a machine-interpretable Web. Except
for that, semantic web provides several protocols and query languages, which can be
deployed to query and reason over RDF datasets to infer new knowledge from them.
The ubiquity of Web technologies renders them a viable solution for managing
data coming from things. The Internet of Things is transforming into Web of Things
to leverage the advantages of the Web. The Web Thing Model imposes software
modules that allow the things to easily integrate the Web (with JSON messages).
Consequently, the IoT systems can benefit through the representation and description
of things and their environments and through the semantic annotation of the data
coming from things, leading to better understand them.
The book chapter is organized as: On Section 1.2 Applications, Related Work and
Research Challenges in Semantic Web and IoT are presented. On Section 1.3 IoT
knowledge Representation with Semantic Web technologies is analysed with more
emphasis in Modelling sensors (1.3.1), multimodal events and observations (1.3.2)
and Ontology-based reasoning (1.3.3). In Section 1.4 The Semantic Web of Things
is presented and how it augments the IoT.
1.2 Applications, Related Work and Research Challenges in
Semantic Web and IoT
In this section we describe relevant surveys on the research area. Also, we describe
the research challenges and the applications that Semantic Web can be combined
with IoT. How Semantic Web technologies help IoT systems to address different
issues.
1.2 Applications, Related Work and Research Challenges in Semantic Web and IoT 3
1.2.1 Applications on different domains
1.2.1.1 Smart Home
A useful guide on IoT applications can be found in [1]. It summarizes the related
work and systems of smart home applications using Internet of Things. The articles
examined were collected and created a taxonomy of four classes, formed based on
the content of the articles. [2] deals with the issue of data homogeneity in IoT appli-
cations. The authors propose a framework that uses semantic technology to combine
IoT data obtained from a smart home. The goal is to utilize the data in order to
recognize presence and abnormal activities. [3] focuses on safety services of smart
home applications and proposes a semantic approach for recognizing risk events.
[4] proposes an Ontology-based framework for activity recognition. The framework,
named SOnAr, consists of two approaches, that use ontologies for context-based
activity recognition. In the first approach, activities are recognized with the use of
Ontology Web Languages for reasoning. The second approach combines ontolo-
gies, statistics and Fuzzy Logic in order to recognize more complex activities and
overcome issues of scalability and sensors’ noise.
1.2.1.2 Smart Cities
One of the most common and fast-growing concepts in information technology is
the smart city concept. The definitions and ideas used to characterize a smart city,
however, are just as numerous as the cities themselves. The word "smart city" is used
with various definitions because there is no specific description to match all of them.
In fact, a smart city is known as a network of heterogeneous systems that should be
connected with each other, be orchestrated and also be intelligent. In order to provide
interoperability between such large and diverse systems several common knowledge
representation models are used [5].
A number of projects have been developed like SeeClickFix [6], FixMyStreet [7]
and ImproveMyCity[8] with the aim of providing the community and public author-
ities with an online service to link every issue within their own region. An ontology
is developed for the definition of smart city environments in [9], known as Smart
City Services Ontology (SCSO). It was created on the basis of four intelligent urban
applications including Smart Parking, Smart Garbage Control, Smart Streetlight and
Smart Complaint. Smart Parking was designed to provide information regarding free
parking areas in real time. The Smart Streetlight system provides actual-time audit-
ing of streetlights so that they can operate only when needed. Smart garbage control
system is primarily designed to identify and inform the accountable authorities via
intelligent sensors, waste level in the garbage containers. Finally, the complaint man-
agement system enables citizens to submit reports regarding municipal problems
and allows officials to arrange their settlement.
The Open Traffic Lights (OTL) [10] project proposes a methodology for pub-
lishing traffic lights using an ontology as the knowledge representation model. The
4 1 Semantic Web and IoT
ontology [11] is available under an open license describing the topology of an inter-
section as well the signal timing of traffic signals. Live traffic lights from crossing
points in Antwerp, Belgium are shown in a web application [12]. In addition,in [13],
they tested how to estimate a dynamically changing phase of the traffic lights with
the live OTL dataset of Antwerp.
Smart Cities also consistently support the transition to viable and efficient energy
systems through the promotion of energy efficient policies regarding renewable and
smart energy management and production. Big data analytic services to predict
energy consumption and manage usage patterns were made available through the
Internet of Things (IoT) smart grids. A context-aware framework is proposed by
the authors of [14], which uses sensor information to create a context-aware model.
They created an ontology and a reasoning service for the management of power
equipment.
Efficient urban water management is also a growing challenge due to the in-
creasing population density and the competing pressures towards sustainability that
is related not only with water conservation but also with energy expenditure. The
energy consumption of clean water systems, even in locations with water abundance,
mandates efficient management and waste minimization. The authors of [15] present
a novel cloud platform, that brings together clean and waste systems to deliver
demanding responsive water management. That is achieved by integrating several
sensing and analytical components via a semantic knowledge base. This knowledge
base reuses GIS data alongside dynamic sensor data, social concepts and inference
rules.
1.2.1.3 Transportation
As cities’ population increments, efficient transportation grows critical for quality of
life, economic productivity and the environment. Traffic monitoring, which includes
tracking of a moving or immobilized car and also involves traffic operations to infer
the traffic within the city streets, is an important concern of smart traffic systems. In
the IoT domain, heterogeneous sensors and devices, such as cameras etc., provide
media content that facilitate global interoperability between the physical and the
virtual world.
In [16], the Multimedia Web Ontology Language (MOWL) is used to store
data coming from heterogeneous sensors, such as cameras, WSN, GPS and other
components thus encoding the smart traffic domain knowledge. Incoming data are
first processed and then analyzed to predict real-time traffic conditions and trigger
the appropriate actions.
Similarly, connected cars can easily turn a simple car from a transportation object
to a digital platform for integrating humans with a city. To this end, the cars need
to be informed of their environmental connected services, how they are linked with
each other and finally how to connect with them so that they can benefit from their
use. The authors of [17] developed a semantic repository in which they preserve
data consisting of the car’s view on the real world. The semantic repository regularly
1.2 Applications, Related Work and Research Challenges in Semantic Web and IoT 5
acquires new location-specific information from sensors and devices to satisfy user
requests for service discovery, such as parking spaces and restaurants.
In [18], an ontology was developed for collaborative route planning based on
data coming from navigation sensors (e.g GPS). The model describes interactions
between various types of travelers such as drivers, autonomous cars and pedestrians,
given that they all have some kind of navigation sensors and share navigational
information. Those different kind of travelers share their navigational needs and
desires for meeting various objectives. An algorithm for self driving tourists was
proposed in another study [19]. The model describes the experiences of drivers and
services requested by tourists. It examines traffic information, schedules of touristic
places (i.e. museums), the vehicle’s capacity in fuel as well as the type of fuel and
the time of departure.
ST4RT project [20] aims at providing transformation between various standards
and protocols based on an ontology, resulting in improved interoperability among
different legacy systems of transport organisations. Whenever two systems which
adopt different standards are required to exchange information, the semantic trans-
formation takes place. The main idea is to create associations from the data models
of the two systems to a global reference model.
1.2.1.4 Health and well-being
INTER-Health is a healthcare platform, developed as part of the INTER-IoT plat-
form, a (Horizon 2020) European project. The platform is actually an integration
of other existing heterogeneous IoT platforms, namely UniversAAL and BodyCloud
[21]. This integration intended to provide services that the individual platforms could
not support. The Inter-Health platform focuses on people with food and physical dis-
orders and observes their lifestyle in order to prevent health problems. [22] provides
an overview of Detailed Clinical Models (DCM). A DCM is an information model
that expresses clinical concepts and requirements for clinical information. The paper
discusses the advantages of DCMs compared to other approaches, like the two level
modeling. A framework for distributed e-health records is presented in [23]. The sys-
tem is a unified semantic interoperability framework that uses fuzzy ontology and
consists of three layers. Each layer performs the following operations respectively:
a) storing of heterogeneous health records, b) mapping local ontologies to global
ones, by using relevant algorithms or human expertise and c) user interface through
which medical experts send queries.
1.2.1.5 Security - Safety
Ontologies have become a trend recently for making decisions easier during a cli-
mate crisis (such as floods, earthquakes, forest fires etc.). The enormous flow of
information from humans and sensors is one of the most difficult challenges that the
authorities face during such crisis events. A lightweight ontology was proposed in
6 1 Semantic Web and IoT
[24] to manage climate crisis and combine all relevant aspects of crisis management:
crisis representation, sensor analysis, crisis incidents and related impacts as well as
unit allocation of first responders.
The authors of [25] have suggested a system that lies in the intelligent combination
of devices and human information against human and situational awareness, in order
to encourage security and a secure ecosystem for people. In order to make the use
of deductive reasoning over the gathered information feasible to tackle a range of
urgent situations, such as health-related problems and missing children in crowded
places, the DESMOS ontology has been developed that covers most of the principles
that are related to the identification of critical incidents and the implementation of
risk management processes.
Significant developments have recently been made in technology for autonomous
vehicles, without direct human control. The safety evaluation of the automated
driving functions is an important subject in the automotive industry. Methodically
defined scenarios by experts can help to strengthen engineering and safety research.
Numerous studies have shown that ontologies provide an effective context for var-
ious autonomous vehicles’ applications. Several scenarios for the development of
automated driving services are proposed in [26]. The authors of [27] concentrated
on designing a model for supporting vehicle communication. They described an
ontology that encompasses all possible on-road scenarios. They derived situation-
aware routing protocols based on this model, and created simulated traffic and unique
scenarios that are likely to cause accidents.
A research was performed on the detection of health hazards in metro construction
sites in [28]. Security risk detection in metro construction is a knowledge-intensive
process and is one of the most important tasks while managing risk. The information
is collected mainly in non-structured formats from various sources. Additionally,
each project typically develops its own information system to facilitate decision
making. In the study, an ontology offers a way to standardize and codify knowledge
related to safety risk that can also be distributed between different actors involved
in the project as well as between difference computer systems. The ontology can
also be used in the production of smart applications that can support the detection
of safety risk.
1.2.1.6 Earth observation
Combining semantics with deep learning techniques into Earth Observation data
has been a hot topic during the past few years. The primary reason behind this
tendency is that while Earth Observation data are increasing rapidly, semantics offer
an intelligent fusion between data deriving from heterogeneous sources and high-
level solutions in decision-making issues, as they enhance knowledge discovery.
In most cases, the problems are associated with physical resources management,
environmental protection and monitoring. For this purpose, several ontologies and
systems have been developed in literature. Some of them are presented below.
1.2 Applications, Related Work and Research Challenges in Semantic Web and IoT 7
Most systems combine semantics in order to achieve knowledge discovery by
fusing data from heterogeneous sources [29], [30], [32], [34], [35] while others
are responsible for semantic querying for image retrieval [31]. The main challenge
encompasses, have to do with combining data from heterogeneous sources (e.g.
OpenStreetMaps [30], [34], satellite and aerial images [30], Google Earth images
[34]) which is achieved using different fusing techniques (like FuseNet architecture
[30]). The scope of using semantics in earth observation data has to do with semantic
labeling in most cases [30], [32].
The ontologies developed to map earth observation data are associated with hy-
drological data [33] and environmental monitoring data [35]. More specifically, the
ontology presented in [33] represents sensors, observation and hydrological events
classes, while Modular Environmental Monitoring ontology (MEMOn) [35] builds
a more extensive structure which contains a large amount of aspects like disaster,
temporal, environmental material, sensor, environmental process, geospatial, obser-
vation and measurement and infrastructure modules.
Intelligent Interactive Image Knowledge Retrieval (I3K R) [29] is a system which
conducts semantic-based Knowledge discovery by using EO data archives. The
system uses a hybrid ontology approach to interconnect data from different sources
and DL reasoning services to apply semantic restrictions. PREDICAT [35] is a
system which aims in interconnecting data from heterogeneous monitoring systems
using ontologies, data integration and reasoning techniques to produce knowledge
from existing natural disasters and predict possible future natural catastrophes.
1.2.1.7 Creative industries
According to the Department of Culture, Media and Sport, there is a plethora of
creative sectors that are identified as belonging to the creative industries [36]. This
subchapter encompasses state-of-the-art related work and applications from all sec-
tors except for those related to Cultural Heritage which are presented thoroughly in
the following subsection.
There are great opportunities in creative industries for administering digital con-
tent. In that aspect, an ontology-based framework named as V4Ann was developed
as part of the V4Design platform [37]. Its main purpose was the knowledge repre-
sentation, the semantic aggregation from multiple sources and the combination of
annotations deriving from multimodal analysis results of digital content. Regarding
architectural designs in urban spaces, MindSpaces aims to provide solutions for cre-
ating functionally and emotionally attractive environments [38]. Furthermore, the
i-Treasures platform, based on multimodal fusion and semantic media interpreta-
tion, has created an open and extendable platform which contains a wide range of
captured intangible cultural expressions available in digital form [39]. Finally, the
IoT field includes also wearable devices in which in that scope the WEAR Sustain
network assessed the issues of sustainability and ethics among all creative actors of
the industry into a unified knowledge base [40].
8 1 Semantic Web and IoT
1.2.1.8 Cultural Heritage
In the Cultural Heritage (CH) domain, the concern and actions about cultural arte-
facts protection are of great significance, thus semantics are deployed, spanning from
semantic analysis of sounds deriving from sensors [41], preventive conservation with
automatic detection of potential hazards and execution of suggested possible solu-
tions [43], weather, environmental and structural monitoring [44], advanced predic-
tive analytics [50], wireless low-cost non-invasive systems for monitoring parameters
of space [52], merge of different ontologies with advanced reasoning capabilities on
top [54], to a system for automated regulation of microclimate parameters [62].
Intertwined with the CH domain is also the field of tourism. Efforts towards
smart discovery of cultural routes incorporating data from heterogeneous sources
and geospatial semantics have been conducted [42],[60],[61]. Another aspect within
the correlation of CH with tourism, is the provided user experience, where attempts
to improve the UX and enhance the visitor’s interaction with cultural objects have
been committed [45], [47], in some cases even by deploying Augmented Reality
features [49]. In that direction, personalization techniques that mine user’s interests
into CH [51] or extraction and identification of personas were investigated [53], [58].
The modeling to predict and describe the dynamics of interaction processes was
within research scope [55], [56]. The necessity of adequate ontological modeling led
to further semantically enriching museum collections [46] and hidden knowledge
extraction and representation [48], even to development of semantically enriched 3D
models [59].
Finally, big data infrastructures to administer cultural items digitally were con-
structed and tested, tendering a variety of services unto the point of incorporation of
several aforementioned modules [57].
1.2.1.9 Manufacturing
Industry 4.0, known as the Fourth Industrial Revolution, combines automation and
digitalization in manufacturing technologies. Many of the previous old manual de-
vices are now supposedly supplanting by new devices and autonomous systems. The
devices (physical or virtual) are continuously getting smarter and Artificial Intelli-
gence (AI) capabilities are being placed in objects, robots and spaces, enabling them
to comprehend their environment and reason, interpret and learn. As the number
and intelligence of “things” increases, there is a need to shift from statically inter-
connected IoT nodes to autonomous and collaborative entities in Industry to harness
intelligence and support dynamic connectivity, interactivity and decision making
augmenting the operational value of the industry.
The combination of AI and Semantic Web technologies will lead to a solution for
a number of complicated problems related to interoperability, automated and self-
configurable systems such as those from Industry 4.0. Through this combination it
can be achieved an holistic view of a Factory of the Future (FoF) enabling better
decision making across different management layers to reduce overall complexity.
1.2 Applications, Related Work and Research Challenges in Semantic Web and IoT 9
This holistic approach includes the interconnection of heterogeneous data sources,
the production chain, business processes and so on. Another example for the in-
tegration of Semantic technologies is on the Autonomous systems. Machines can
communicate and exchange information with other machines under the same vocab-
ulary, in order to succeed a common target. New machines can participate easily in
the production chain without the necessity delegate a heavy work force on this task,
by simply creating interoperable services. Faulty devices are easily being substituted
by discovering new devices with similar functionality to prevent downtime during
the production process.
The semantic technologies in an Industry 4.0 platform can be integrated at the edge
or at the cloud layer, depending on the use case application. The semantic knowledge
layer receives data after a middleware uses standard protocols like MQTT, OPC UA
or HTTP(S) and formatting the data using open standards, like OPC UA, PPMP,
PackML. Then, it employs defining and sharing of semantic information to allow for
easier analysis across different systems [63].
There are many studies which [64], [65] model industrial products and services.
In [66] an approach is presented for integrating IoT to a MAS (Multi Agent Sys-
tems) based manufacturing environment, semantically enriched the relevant ontology
and its validation through a Hardware-in-a-Loop simulation utilizing a gamifica-
tion system. In [67] the SAREF ontology was extended with the creation of the
SAREF4INMA ontology for describing the Smart Industry and Manufacturing do-
main. SAREF4INMA is based on several standards and IoT initiatives, as well as
on real use cases, and includes classes, properties and instances specifically created
to cover the industry and manufacturing domain. [68] proposes the implementation
of MTConnect as machine-interpretable ontology (OWL) to achieve two things:
Firstly, to preserve the semantics of the reference within the model and secondly,
to enable its interlinking with other datasets to form the basis of the Industry 4.0
vision. MTConnect defines specific data patterns to facilitate healthcare monitoring
of machine tools. Thus, it provides the foundations for predictive maintenance to
reduce the possibly premature exchange of expensive machine parts or to prevent
entire machine outages due to ruptured parts based on sensor data. Authors uti-
lized the existing ontologies such SSN, SAREF and and SEM for their ontology.
Some queries in SPARQL as a proof of concept for ontology completion were also
mentioned. Industrial robots used in manufacturing kitting stations are modelled in
ontologies presented in [69] in a project from the National Institute of Standards and
Technology (NIST).
The main challenges for the semantic technologies in IoT in Industry are in
setting ontologies for integration and interoperability between the already existing
old industry standards, use of existing ontologies (e.g. SSN, SAREF etc) to make
applications for industry 4.0 using sensor data provided by autonomous systems and
the execution of exemplary use case scenarios in real industrial conditions.
10 1 Semantic Web and IoT
1.2.1.10 Agriculture - Farming
In agriculture, there is very fast-growing trend with smart devices entering the fields
and helping farmers to comprehend better the crops and their production. This trend
is called precision agriculture and currently is generating huge volumes of raw
data from IoT sources such as: chemical sensors, electro-chemical sensors, drones,
weather stations and so on. Those thousand of lines raw data are meaningless and
isolated, and therefore they do not add extra knowledge to the farmer. Agriculture
activities are based on a multiparameter knowledge with many interconnections
between the parameters. The efficacy of data derives from context and meaning, as
well as its combination with other data from different agriculture sources. Semantic
technologies can provide practicality to the agriculture/farming data by providing
common interchange data formats. Also, through SW new knowledge can be provided
through the use of reasoners.
Semantic resources are typically divided in two different categories for general
agriculture or specialized domains of agriculture. Several significant agriculture on-
tologies are OntoAgroHidro [70], Crop ontology [71], GCP ontology [72], Agropor-
tal [73], Agricultural Technology Ontology [74], Citrus Ontology [75], Agriculture
Activity Ontology (AAO) [76], AgOnt [77], Agronomy Ontology [82]. Projects like
Agrovoc [78] consists of +36,000 concepts and +750,000 terms in up to 35 languages,
have provided with structured vocabularies for the agriculture domain. The Global
Agricultural Concept Scheme (GACS) [79] contains in its files of interoperable con-
cepts the schemes related to agriculture from AGROVOC multilingual agricultural
thesaurus (35,000 concepts), the CAB Thesaurus [80] (140,000 concepts) and the
NAL Thesaurus [81] (53,000 concepts).
Agriculture requires common data schemes for semantic web technologies to
render plausible the transfer of semantically described data and the development of
common ontologies. One such a standard is known as: The Agricultural Metadata
Element set (AgMes) [83]. AgroRDF [84] is one of the major standards for data
exchange, which is designed specifically for agricultural data. The applications with
agriculture semantic technologies are divided mainly into those different categories:
Knowledge based systems, Remote Sensing, Decision Support and Expert Systems
[85].
1.2.2 Main Research challenges
The idea of integrating physical objects and communicating with each other is not
a new one. Various technologies and standards have been proposed until today.
Many of those technologies have been used and established the vision of Web
of Things. Although, one of the most crucial challenges until now is to address
the interoperability of those things, technologies and standards under a common
framework.
1.3 IoT Knowledge Representation with Semantic Web Technologies 11
The spread of IoT and consequently the WoT is expected to fetch a huge amount
of real-time sensing and not only, data to the Web services. This leads to a vast
number of information and services that needs to be interpreted. In contrast to the
traditional web pages and documents that are accessible to the present web, WoT
will bring dynamic content that will rapidly change due to the nature of IoT data. The
search engines of WoT should efficiently provide real-time data and discover dynamic
services. In order to enable, a catholic web of things framework, research community
will need to be based on open standards independent of particular vendors, in which
every developer can extend and enrich the different developed technologies.
One other important research challenge is the research on smart object integration
on creating context-aware ambient environments. New solutions need to be suggested
in order to address this challenge. The research here is basically focused on health
domain, security and safety, retail market and smart homes.
Security, privacy and trust among different smart objects and users are a vital issue
on WoT. The widely utilization of REST interfaces has enabled the use of similar web
security approaches in WoT also (based on HTTP protocol). As the significance of
IoT data is growing rapidly, the research in trustworthiness is a very important issue,
as everyday human applications are based on those data. Trust issues are integrating
the interaction issues among smart objects. Advancements in the social WoT have
suggested new solutions to those research challenges [86].
Nowadays web applications and services are based on software for specific tasks.
However, they lack flexibility when they take into consideration human in the loop.
One of the most crucial future research challenges is when the smart object will
have to interact with humans. Over the next years, there will be virtual smart objects
that will understand human emotions, experience and reactions, providing common
sense. This will become true by extensive research combination of cognitive psy-
chology, social IoT and advanced artificial intelligence techniques. Semantic web can
augment this research field by capturing human knowledge, feelings and experiences
in different domains (health, social life, relationships and so on.)
1.3 IoT Knowledge Representation with Semantic Web
Technologies
In this section we describe the semantic web technologies and standards, that are most
popular for IoT representation. How can the use of powerful formats add structure
and meaning to the content of data coming from IoT devices and interlink related
data.
12 1 Semantic Web and IoT
1.3.1 Modelling sensors
Sensors embedded in devices that are attached on the human body or sensors directly
placed on the human body, are called wearable sensors [87]. The existence of such
sensors in mobile and wearable devices has led to their extensive use in activity
recognition and fall detection tasks. Accelerometers and gyroscopes are the most
popular ones, with accelerometers being the most effective in recognising activities
when used individually. Gyroscopes are also quite popular, however, they are mostly
used in combination with the accelerometers. Accelerometers are known to perform
well in recognizing activities in general but they are more successful in activities
with repetitive movement [87], since they measure a moving object’s magnitude and
direction. They usually fail to recognize similar activities when used individually
[88], thus it is more effective to use them along with other inertial sensors to improve
the performance of a human activity recognition system. Gyroscopes perform well
in recognizing an object’s orientation because they measure the rotation speed [89].
Gyroscopes are widely used in activity recognition studies, but most of the times
not as the only sensor. Fusion of such sensors, whether performed before or after the
classification algorithm, is found to improve the recognition rates of a human activity
recognition system, since one sensor may capture movements not well detected by the
other [90]. Magnetometers are a third kind of wearable sensor that is also explored in
activity recognition studies; their individual performance though is poor and they are
mostly used in combination with the other sensors. The aforementioned sensors are
found in all smartphones and smartwatches, which is the main reason they are widely
utilized for activity recognition studies since it is easy to extract their measurements.
The sensors are most often triaxial and they produce three vectors of raw signals,
one for each axis of the Cartesian reference system [91].
The raw data consist of three vectors of values, each one relevant to one axis of
the Cartesian system. After the extraction of the raw data, is the preprocessing stage,
which may include filtering and/or the normalization of the data to eliminate signal
noise. Features are afterwards extracted by a time window, which is employed because
it makes two signals comparable. Feature extraction retains valuable information
from the signals [92]. The two basic categories of extracted features are time domain
and frequency domain and a list of the ones computed in most studies can be
found in [92]. After the extraction of features, a feature selection method may be
applied to identify which features will potentially assist in the improvement of the
recognition of activities and to eliminate large feature sets [92]. The HAR framework
is concluded with the classification process, where a classification algorithm is
applied to recognize the activities.
Activity recognition tasks are actually multiclass classification problems. The
choice of the classification algorithm is driven by various parameters like the types of
the recorded activities, the type of data and the extracted features. Some classification
algorithms are found to perform very well in the majority of such studies, like Support
Vector Machines (SVM), Naive Bayes (NB) and Decision Trees [92]. If a system
consists of different sensors and there is a need to utilize the information provided by
all of them, fusion methods are applied. Fusion is the combination of information and
1.3 IoT Knowledge Representation with Semantic Web Technologies 13
that can be performed after the classification process to combine the classification
results of each sensor or at earlier stages, before the data enter a classifier, where it
combines the extracted features of different sensors [93].
1.3.2 Modelling multi-modal events and observations
1.3.2.1 Location
Data semantics are extensively used in location-based services (LBS), in order to
find and integrate the information related with the users. Several LBS were analyzed
and recorded in [94]. First they have classified the LBS data based on relevant
definitions and use. A distinction was made between Domain Data, Content data
and Application data where the Domain Data include spatial and temporal concepts
(e.g. location, position, time etc.), Content data mainly describe specific content, and
finally, Application data comprising of the actual services and user profiles.
1.3.2.2 Activities - Events
Event-Model-F [95] describes a process for identifying and describing real word
events. It is based on DUL and follows the "descriptions and situations" (DnS)
ontology design framework [96] for modelling different concepts of events, such
as object attendance, relationships, and different meanings of the same event by
introducing six ontology design patterns. In addition to the DnS model, Event-
Model-F implements a number of internal representations to describe relationships
among events, such as causality and correlation. The following figure (Figure 1.1)
[97] describes the pattern of Event-Model-F correlation of Events.
The Simple Event Model Ontology (SEM) [98] is an attempt to establish an
ontology model for events with no extreme semantic restrictions. The open nature
of the Web itself and the necessity to design various perspectives of the same event,
support this decision. The proposed ontology has core classes such as Event, Actor,
Place and Time and corresponding properties that allow us to model fundamental
facts. This also involves means to express some restrictions related to different points
of view, namely: (1) Event bounded roles, (2) time bounded validity of facts (e.g.
type dependent type or roles) and (3) attribution of the authoritative source of a
statement. The following figure (Figure 1.2) illustrates the main principals of this
ontology.
1.3.2.3 Video
A minimum collection of properties capable of defining media resources such as
videos, images and audio files has been developed by the W3C Media Annotation
14 1 Semantic Web and IoT
Fig. 1.1 Event-Model F
Fig. 1.2 Simple Event Model
Working Group (MAWG). The implemented annotation model [99] consists of sev-
eral descriptive and technical properties that can be used to describe any kind of
multimedia resources along with their technical characteristics. Among the descrip-
tive properties are the title, the language, the creator, the publisher etc. The technical
properties refer to more technical aspects of the a media resource like the compres-
sion rate, the format etc. The descriptive properties have been defined in such a
1.3 IoT Knowledge Representation with Semantic Web Technologies 15
way that the ontology can be considered as media-agnostic since they can describe
any multimedia object. On the other hand the technical properties are specific to
particular types of multimedia objects. The following figure (Figure 1.3) provides a
summary of the key concepts of the given ontology [100].
Fig. 1.3 Ontology for Media Resources
1.3.2.4 Text - social media
Named entity linking (NEL) or Entity linking (EL) is the task of mapping an entity
appearing in a document to a respective Knowledge Base (KB) identifier and is
considered as a fundamental step to semantic language understanding. Considering
the abundance of text circulating online and the quantity of pieces of information
to be represented in text analysis systems, being able to correctly discern among
homographic entities determines the KB’s quality and subsequently, the entire sys-
tem’s efficacy. Word polysemy, abbreviations and acronyms, spelling variations and
synonyms are some of the factors that pose challenges towards entity disambigua-
tion and correct linking. The application possibilities are numerous and range from
knowledge graph construction [101] to question-answering systems [102] and in-
formation retrieval [103]. Traditional NEL approaches relied on text-based models
which leveraged linguistic hand-engineered features [104] and machine learning
classifiers (SVM) [107]. Modern systems exploit large knowledge bases (DBpedia,
Wikipedia, WordNet) to create knowledge graphs [105] and deep learning techniques
[106] which leverage both global and local features to achieve document level dis-
ambiguation; character and word embeddings, an attention mechanism and a CRF
layer. Local and global features are combined to tackle disambiguation in [108],
which proposes a Personalised PageRank-based approach, a popular Random Walk
(RW) algorithm. Lastly, in [109] a RW variant (random walk with restart - RWR)
16 1 Semantic Web and IoT
and a high-coherence densest subgraph algorithm are combined to create Babelfy,
an integrated approach to EL and Word Sense Disambiguation (WSD).
1.3.2.5 Modelling domain - context
The primacy in cultural heritage domain is owned by CIDOC Conceptual Reference
Model (CRM) [110] which is responsible as both a theoretical and a practical tool
to integrate information in the field of cultural heritage. It provides definitions and
structures depicting concepts in the domain enabling querying and investigation of
such data. It is the nurture of over 20 years of development and maintenance by
the CIDOC Documentation Standards Working Group and more recently by the
CIDOC GRM SG. Since 2006 it has been recognized as an official ISO standard
(ISO 21127:2014).
Ontologies in health domain can capture information like patient profile, including
physiological information, personal information, activities and information specific
to the health status of the patient. An abundance of ontologies have been developed
for this scope, to support smart home capabilities, provide smart assisting living
technologies, improve patients’ everyday activities and enhance patients residence
in healthy environments. Some of them are presented below.
A plain vocabulary to describe people, activities and information about their
relations is provided by Friend Of A Friend (FOAF) [111], [112] ontology. This
ontology is often used to describe personal information, social connections and
networks between people, for instance their membership in groups. People, described
as instances of foaf:Person class, can contain many properties like name, email
address, image and age. On the other hand, General User Model Ontology (GUMO)
[113] offers a more uniform representation of user models. The ontology describes
a user from many different perspectives: contact information, demographics, ability,
personality, characteristics, motion, role, nutrition, facial expression and emotional,
physiological and mental state.
A more extensive ontology for capturing patient information is AHA [114]. AHA
ontology uses information coming from wearables to support Ambient Assisting
Living environments. The main scope is monitoring activities and extending smart
home abilities to assist in lifestyle profiling and healthy ageing issues. The ontology
captures body measurements information (such as weight, height), activity-specific
information (such as activity levels, energy expenditure, body position that each
activity affects) and health state information (such as general health, heart rate,
temperature). AHA ontology schema is presented below.
1.3.3 Ontology-Based Reasoning
This section is related to semantic complex event processing, queries, reasoning
rules and algorithms as well as different reasoning frameworks.
1.3 IoT Knowledge Representation with Semantic Web Technologies 17
Fig. 1.4 AHA ontology schema
1.3.3.1 Reasoning Frameworks
Description Logics (DLs) [115] are a group of knowledge representation frameworks
characterized by logically driven semantics and well-defined inference structures.
The key components are classes representing sets of objects (e.g. Person), properties
representing entity associations (e.g. livesIn) and individuals representing individual
objects (e.g. Tom). Description Logics provide a powerful set of reasoning frame-
works. Pellet [116] Racer [117], Fact++ [118] and Hermit [119] are examples of
state of the art implementations of such frameworks. Starting from basic definitions,
such as Person, it is possible to describe more complicated concepts. For example,
the concept ∃has Father .Per son describes those objects that are related through the
has Father property with an object from the class Person. A DL knowledge base K
typically consists of a TBox T (terminological knowledge) and an ABox A (assertional
knowledge). The TBox includes axioms describing possible ways of associating do-
main objects. For example, the TBox axiom Cat vAnimal asserts that all objects
that belong to the class Cat, are members of the class Animal too. The ABox con-
tains axioms which define entities of the real world, for example Cat(Daisy) and
isLocated(Daisy,garden) express that Daisy is a cat and she is located in the garden.
The table below summarizes the set of TBox and Abox axioms.
The OWL language is commonly used within the community for ontology devel-
opment and data representation. DLs have greatly influenced the design of OWL and
especially the formalization of the semantics and the choice of language construc-
tors. OWL comes in three highly articulate dialects: OWL Lite, OWL DL and OWL
Full. The most concise of the three is OWL Full: it does not place any limitations on
18 1 Semantic Web and IoT
Table 1.1 TBox and ABox axioms
Name Syntax Semantics
Concept inclusion CvD C IvDI
Concept equality C≡D C I=DI
Role equality R≡S RI=SI
Role inclusion RvD RI⊆SI
Concept assertion C(a)aI∈CI
Role assertion R(a,b) (aI
,bI) ∈ RI
the use of OWL constructors, nor does it raise the distinction between individuals,
properties and class. However, this kind of expressiveness comes at a cost, namely
the loss of decisiveness which makes it difficult for the language to be implemented.
An updated version of OWL (that is known as OWL 1) is the OWL 2 language
[120]. It extends OWL 1 with eligible constraints on cardinality; thus one may
argue, for example, that a social event is an event with more than one actor: So-
cialEvent ≡Event u ≥ 2 hasActor.Person. Another notable characteristic of OWL 2
is the expanded relational expressiveness provided by the implementation of axioms
(property chains) of complex-property-inclusion. To preserve decisiveness, these
axioms are subject to a regularity constraint, which cyclically disallows the concept
of properties.
A lot of effort has been dedicated to incorporating OWL with rules. A suggestion
for this aim is the Semantic Web Rule Language (SWRL) [121], in which rules
are represented under the standard first order logic semantics. Allowing class and
property predicates to exist without any constraints in the head and body of a rule,
SWRL maximizes the connections between the OWL and rule elements, while at the
same time making the synthesis undecidable. Many ideas have addressed syntactic
constraints on rules [122],[123] as well as their descriptive intersection of Description
Logic Programs (DLP) [124]. The DL-safe rules implemented in [122] for example,
specify that rules apply only over known individuals. It should be mentioned that DL
reasoners offering support for SWRL actually implement a subset of SWRL based
on this notion of DL-safety in practice.
From a separate point of view, a variety of methods have studied the fusion of
annotation models and rules based on mappings on rules engines of a sub-set of
ontology semantics. For example, [125] describes the grammar of pD∗as a weaker
version of OWL Full. In this grammar classes can also be considered as instances
and they are generalized to refer to a broader subset of OWL vocabulary. Driven by
the entailments of the pD∗grammar and DLP, the OWL 2 RL profile of semantics is
realised as a partial axiomatisation of the OWL 2 semantics in the form of first-order,
known as OWL 2 RL/RDF rules. Rules defined by users over the ontology allow
richer semantic relationships to be articulated outside the descriptive capabilities of
OWL, combined with ontological awareness and rules.
SPARQL [126] is a declarative language which the W3C recommends to extract
and update information within RDF graphs. It is an expressive language, which de-
1.3 IoT Knowledge Representation with Semantic Web Technologies 19
scribes complex relations between entities. The syntax and difficulty of the SPARQL
query language have been studied relatively technically, showing that both SPARQL
algebra and relational algebra share the same expressive power [127]. SPARQL is
mainly known as a query language for RDF, however it can define SPARQL rules by
using the CONSTRUCT graph format, which can generate new RDF statements by
merging existing RDF graphs. These rules are described in terms of a CONSTRUCT
and a WHERE clause: CONSTRUCT specifies the graph patterns, that is the set of
RDF triple patterns that should be ingested to the underlying RDF graph when the
graphs in the WHERE clause fit successfully.
Finally, the SPARQL Inferencing Notation (SPIN) [128] is an attempt to simplify
the interpretation and execution of SPARQL rules on top of RDF graphs. Using
SPIN, SPARQL queries can be stored as RDF triples along with any RDF ontology,
allowing RDF instances to be connected to the related SPARQL queries, as well as
sharing and reuse of SPARQL queries. SPIN follows the interpretation of SPARQL
inference rules that can be used by iterative rules implementations to extract new
RDF statements from existing ones.
1.3.3.2 Fusion
One of the most important issues in the IoT sensor networks is the data management.
Sensor networks are facing resource constraints problems due to low battery power,
limited data processing capabilities, limited communication resources and a small
amount of memory. Furthermore, in many applications there are data coming from
many heterogeneous data sources that needs to be compared, combined and corre-
lated between each other. In this way, depending on the applications appropriate data
aggregation systems must be implemented for the processing of the data at the edge
or in the cloud.
Semantic Web helps enabling interoperability among data from different sources
through the content annotation. To become retrievable, data coming from sensor
network systems should be annotated.
The fusion through semantic technologies is realised through different structures.
For example, in text fusion, it is important to fuse statements and assertions from
different sentences, tables, or paragraphs to define definitions, objects, and their
semantic relationships. Another important role for semantic fusion is when there is
a need to fuse ontologies. The majority of researchers are using available ontologies
and in most cases there is a need to combine existing ontologies under the same
framework. When multiple ontologies are used under the same development, a
mapping of ontologies is important to define the concept representations of the
various ontologies relating to the same domain [118]. This can be done by using
specific properties like owl:equivalentClass and rdfs:subClassOf.
There are many studies in semantic fusion. In [129] summarizes the implemen-
tation of a functional semantic fusion system for live content from the Web. In [130]
a service-oriented platform dedicated to fusion processes has been presented. The
underlying common language for services is focused on a collection of ontologies
20 1 Semantic Web and IoT
that allow for the representation and reasoning of various objects, circumstances
and possible threats, and so on. In [131], a use case is proposed that represents the
development of a current and future consumer knowledge base, leveraging of social
and connected open data on the basis of which any company could infer useful
information as a decision-making support. Semantic technologies perform seman-
tic aggregation, persistence, reasoning and retrieval of information, as well as the
triggering of alerts over the semantized information.
1.3.3.3 Validation
The following section presents two approaches for validating RDF data, the Shape
Expressions Language [132] and the Shapes Constraint Language [133]. Both share
the same goal, that is to provide a framework for validating RDF data.
ShEx is a language for describing RDF graph structures. The basic model of
this language, also known as a ShEx schema, contains all the requirements that
the RDF data graphs under investigation must fulfill in order to be considered as
valid. For example, a requirement could be the datatype of the involved subjects or
the combination of subjects, predicates and objects. Based on a list of predefined
requirements, the RDF data is tested against it and a validation report is being
produced consisting of the parts of the RDF data that do not align.
Another method for validating RDF graphs is called Shapes Constraint Language.
Similarly to ShEx, in SHACL a list of pre-defined properties define the requirements
that an RDF graph should fulfill in order to be considered as valid. Those require-
ments are called shapes graphs in SHACL and the data that is validated against a
shape graph are called data graphs. Given a shapes graph and a data graph the result
of the validation process is also an RDF graph that reports the conformance of the
data graph to the shapes graph.
1.3.3.4 Temporal Reasoning - Stream Reasoning - CEP (complex event
processing)
Incorporating the time dimension aspects in both modeling and reasoning, implicitly
is granting supplementary temporal assets in objects and knowledge representation
in general, thus enhancing the feasibility in exploiting information in order to realize
complex event processes to a greater extent, encompassed within the domain of time.
The basic principle that needs to be abided by so as to indite the essence of time
is the time instant, an infinitesimal moment in time, based on which more com-
pound temporal concepts are able to be defined, such as time intervals, duration,
commencement and conclusion of events, periodicality, schedules and so on [134].
Based on those structural entities and with the adjustment and application of ad-
vanced reasoning techniques, one can monitor the temporal flow of occurrences of
events, time-irrelevant instances alterations over time and evolution [135].
1.3 IoT Knowledge Representation with Semantic Web Technologies 21
What differentiates the temporal reasoning from stream reasoning is the rapid
frequency in which novel data are acquired and/or metamorphosed and the urgent
need for live reasoning. In more detail, it requires fully-automated fault-tolerant
pipelines along with heuristic optimization techniques to be able to address to nearly
instantaneous rearrangement demands based on live triggers [136].
1.3.3.5 Querying - Linked Data
The last decades a great amount of data has been available through web technologies.
Most of these data are associated with geolocation information. Since geolocated data
are rapidly increasing, there comes the need to use and combine these information
to extract hidden knowledge. Semantic web technologies are responsible for such
tasks as they use reasoning techniques to combine data from heterogeneous sources,
supporting in that way more complex semantic queries. Some of the most popular
sources are DBpedia, Open Street Maps and Wikidata and their usage in semantic
reasoning systems is shown below.
In [137] the authors propose a system which utilizes Open Street Map data to
support more complex reasoning rules. The system uses an information broker to
apply rule-based reasoning and extract topological relations among entities. More
specifically, OWL is used to represent semantically the information and SQWRL
rules and vertical plane sweeping technique are used as spatial reasoners. The vertical
plane sweeping technique calculates the overlapping polygon, given two polygons.
SQWRL rules build on top of the ontology to specify some standards that extract
hidden knowledge. In this work the standards are associated with travel planning and
footways retrieval.
In [138] OSM is used to gather georeferenced information about points of inter-
est (POIs). The collected information contain points, polylines or polygons and a
combination of both indicating the relation between them. The purpose is to detect
human activities happening in nearby locations. The methodology creates a connec-
tion between human activities and POIs or periods of time. A DL reasoning service
defines rules for grouping a number of human operations per category of point of
interest and a number of human operations in a specific period of time. The system
also predicts human activities according to the popularity of POIs using High Level
Representation of Behavioral Model (HRBModel).
SPARKLIS [139] is an online tool which combines Natural Language Processing
(NLP) with linked data (such as DBpedia, Wikidata, etc.) to apply semantic search.
Natural language expressions are used to represent semantic queries and an auto-
complete function is provided to fill the queries with possible information. More
specifically, the tool consists of three parts: the query as expressed in natural lan-
guage, the query related terms and the results of the executed query. The subsequent
example (Figure 1.5) displays the natural language query and results, while Figure
1.6 shows the same query as executed in SPARQL.
22 1 Semantic Web and IoT
Fig. 1.5 SPARKLIS natural language queries and results
Fig. 1.6 SPARQL query in DBpedia expressing SPARKLIS natural language query
1.3.3.6 Handling Noise, Uncertainty and Imperfect Information
As masses of data have grown tremendously during the past years due to, but not
exclusively, the eruption of the IoT field, it only seems logical to have arisen issues
regarding the quality of such data. Towards this direction several state-of-the-art
frameworks have been proposed to address malfunctions deriving from data noise,
data uncertainty and imperfect information [140].
According to [141], a logical reasoning model has been used to predict missing
data. Grounded on ontological domain knowledge along with a satisfactory dataset
of statements, logical reasoning can track inconsistencies and infer new statements
as predictions of missing data.
Fuzzy reasoning, encompassing all the properties defined in fuzzy logic theory
described in [142], and more specifically non-monotonic reasoning is based on the
concept that an assertion can be generated from premises not entirely specified, but
in the occasion of an exception emerging the conclusion can be withdrawn [143].
Unfortunately, experiential research regarding adding non-monotonic layers upon
reasoning to deal with uncertainty and conflicting data is sparse and not systematic
as in the case of [144], where a rule base compression approach is suggested for
the decrease of non-monotonic rules, or in the case of [145] where a framework,
called FUSE, integrating fuzzy reasoning and semantic reasoning was developed
1.4 The Semantic Web of Things and how it augments the IoT 23
towards a unified reasoning process for the provision of personalized learning rec-
ommendations adaptively and semantically. In addition, a proposal presenting fuzzy
analogical reasoning has been conducted where the case study of MiMo incorporat-
ing soft computing showcases the evaluation [146].
Finally, at the exertion of tackling the nuisance of imperfect information upon
reasoning, several investigations were completed, such as in alternating-time tem-
poral logic (ATL) about responsibility in multiagent systems [147] or agents with
perfect recall where the past is not forgotten in nested games [148]. Furthermore,
investigations towards Graded Computation Tree Logic with finite path semantics
(GCTL*f) under imperfect information settings were performed [149].
1.4 The Semantic Web of Things and how it augments the IoT
With the advent of IoT hundreds of sensors, smart devices and smartphones have
been deployed in our everyday lives. The result of this is tremendous amounts of
data with great differences in formats and domains. This has posed great challenges
for machines to understand information and extract knowledge from those data. For
better representation of IoT different data research studies have proposed different
techniques to enable machines to intelligently understand heterogeneous data. Se-
mantic Web of Things (SWoT) is a continuation of World Wide Web that tries to
solve the problems arised from the heterogeneous systems and provides a better un-
derstanding of the different IoT domains. Web of Things’ main purpose is to enable
interoperability across IoT platforms and application domains. Overall, the WoT’s
purpose is to uphold and complement existing IoT standards and solutions[150].
With the semantic technology in Web of Things the domain knowledge and
background information are combined with sensor data, making machines easier to
understand and process. Moreover, semantics provide a coherent description archi-
tecture that enhance information and the exchange of knowledge between variable
sensor nodes. Before WOT, sensors and Web world have been completely discon-
nected. With WOT IoT related data on the Web would help users in different domains
by accessing directly sensor data and monitoring the real world parameters integrated
with similar context information from the Web.
In order to meet WOT the IoT world, large-scale open interfaces and data formats
need to be optimized and incorporated with their relevant IoT counterparts [151].
Generally, IoT users are interested in real-world situations and knowledge, rather
than in sensing systems and their raw data. Through SWoT, there are the appropriate
abstractions to map sensors and their raw output to real-world entities with real
semantics. To realise the SWoT researchers extend the IoT with all the remarkable
features of Semantic Web: a) widely use URIs and HTTP, b) connecting of domain
models through interoperable references, c) use of common standard languages and
d) domain expressiveness through extrapolation of logical sequences. Some other
challenges that SWoT tries to solve are: i) gradually growing IoT ecosystems with
many individual devices; ii) ability to interconnect devices from different vendors;
24 1 Semantic Web and IoT
iii) ability of open source developers to develop software applications for IoT envi-
ronments; iv) develop applications for generic domains exploiting data from various
sensors.
1.5 Conclusion
The Internet of Things (IoT) is becoming increasingly popular and its implementa-
tions are facing huge proliferation leading to a new digital ecosystem. IoT platforms
are essentially the linchpin of a comprehensive IoT solution as they allow the col-
lection and analysis of data produced at endpoints, spawning the growth of big data
analytics and applications.The rapid increase in the number of network-enabled de-
vices deployed in real world, enhanced by information processing capabilities, has
created vast quantities of databases. As IoT is based on a wide variety of differ-
ent heterogeneous systems and technologies, there is no standardized language for
data representation and processing. This has contributed to a large number of IoT
systems that are incompatible. Thus, it is very difficult for data scientists to extract
information from the huge number of data provided by the IoT applications every
second.
Semantic web technologies try to overcome such challenges. Semantic web lever-
ages web standards and semantic technologies to interconnect all types of devices
by transforming raw sensor data into high-level knowledge that is understandable
by humans and machines. Interoperability is one of the most important challenges
in an IoT environment, where different devices, services and entities try to connect
each other. Semantic modelling produces a definite scheme of the data meaning in
a structured way by combining application knowledge and context-relevant infor-
mation with sensor data. The ontology - based development, which is a domain of
semantic modelling, of IoT frameworks can lead to universal IoT solutions multiply-
ing the benefits of IoT.
In this book chapter, we provided an overview of the current trends in applica-
tion of semantic technologies in the IoT domain. We provided research studies on
reasoning, aggregation, fusion and interpretation solutions that aim to intelligently
process and ingest sensor information, infusing also human awareness for advanced
situational awareness. Finally, issues around Web of Things and how it augments the
IoT are discussed.
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