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Semantic Interoperability for the Web of Things


Abstract and Figures

This paper is co-authored by an informal group of experts from a broad range of backgrounds all of whom are active in standards groups, consortia and/or alliances in the Internet of Things (IoT) space. The ambition is to create mindshare on approaches to semantic interoperability and to actively encourage consensus building on what the co-authors regard as a key technical issue.
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Semantic Interoperability for the Web of Things1
Murdock, Paul
Bassbouss, Louay
Fraunhofer FOKUS
Deutsche Telekom
Bauer, Martin
Ben Alaya, Mahdi
Bhowmik, Rajdeep
Binghamton University
Brett, Patrica
Chakraborty, Rabindra
Dadas, Mohammed
Davies, John
BT plc
Schneider Electric
Diab, Wael
Drira, Khalil
Eastham, Bryant
Insight Centre for Data Analytics
El Kaed, Charbel
Schneider Electric
Elloumi, Omar
Girod-Genet, Marc
Hernandez, Nathalie
Hoffmeister, Michael
Krypton Brothers
Jiménez, Jaime
Kanti Datta, Soumya
Rockwell Automation
Khan, Imran
Schneider Electric
Kim, Dongjoo
1 This document is available under a Creative Commons Attribution 4.0 International License.
This paper is co-authored by an informal group of experts from a broad range of backgrounds all of
whom are active in standards groups, consortia and/or alliances in the Internet of Things (IoT) space.
The ambition is to create mindshare on approaches to semantic interoperability and to actively
encourage consensus building on what the co-authors regard as a key technical issue.
The paper
Considers the value associated with interoperability in the IoT context and suggests that building
mindshare across the industry on semantic approaches is one of the keys to unlocking that
Introduces foundational interoperability concepts and provides a discussion of metadata, the
rationale for sharing metadata, and the requirements for ontologies
Introduces ontologies and discusses their specific relevance to interoperability and significance
in the IoT context
Provides examples of modular ontologies, overviews semantic annotation and tagging, and
highlights strategies for scaling
Draws conclusions and makes a number of recommendations
The document is made available under a Creative Commons Attribution 4.0 International License.
The Internet of Things (IoT) generates expectations that smart devices can discover their context and
build collaborations with other smart devices and services to create value. For example, smart
devices in the home should be able to discover each other and to work together to both enhance the
comfort and security of the home owner and to improve the efficiency of the home. When driving
into the city, a smart car should be able to interact with city services to identify and reserve a
parking place and should be able to collaborate with a personal smart phone to facilitate payment.
The expectation that ad-hoc networks of smart devices and services can be constantly formed and
re-formed to manifest transient value systems is driving the need for broad agreement on how such
devices interoperate and understand each other.
Discovery, understanding and collaboration at this level requires more than just an ability to
interface and to exchange data. Whereas interoperability is “the ability of two or more systems or
components to exchange data and use information" [1] , semantic interoperability “means enabling
different agents, services, and applications to exchange information, data and knowledge in a
meaningful way, on and off the Web[2] .
Semantic interoperability is achieved when interacting systems attribute the same meaning to an
exchanged piece of data, ensuring consistency of the data across systems regardless of individual
data format. This consistency of meaning can be derived from pre-existing standards or agreements
on the format and meaning of data or it can be derived in a dynamic way using shared vocabularies
either in a schema form and/or in an ontology-driven approach. In this paper we will use the term
"data-model based semantic interoperability" to refer to the former, and "ontology based semantic
interoperability" to refer to the latter.
This paper considers semantic interoperability in the context of the Internet of Things (IoT). Note
that we use “IoT” as an umbrella term for the range of emerging technologies which may differ in
scope and reach2, but which enable cross-domain innovation and drive the need for interoperability
at a dynamic level.
2 See [3] for a view on the range of IoT technologies and their scope
Semantic Interoperability as a Value Enabler
There are many analyst studies describing IoT as a broad concept spanning all application domains.
Beecham Research provided some early insight into the scope of domains covered with their "M2M
Sector Map" [4] . A more recent study by McKinsey [5] considers the value potential of IoT in the
context of domains broadly similar to those identified in the Beecham research.
The McKinsey study goes on to provide an estimation of the value that could be unlocked given
interoperability across those domains see Figure 1.
According to McKinsey:
Interoperability between IoT systems is critically important to capturing maximum value; on average,
interoperability is required for 40 percent of potential value across IoT applications and by nearly 60
percent in some settings.
Figure 13 Value Potential Requiring Interoperability
We suggest that this McKinsey finding can be qualified further. Specifically, the full value potential
can only be unlocked if interoperability is implemented in such a way that the dynamic nature of IoT
is fully supported. A clear prerequisite is that ad-hoc, cross-domain systems of IoT elements need to
3 “The internet of things: Mapping the value beyond the hype”, June 2015, McKinsey Global Institute, Copyright (c) 2015 McKinsey & Company. All rights reserved. Reprinted by permission.
be able to establish conversations and build understanding. Several examples of cross-domain use
cases and a discussion on the need for and the value of semantics in sensor applications are
described in [6] .
Hence, the position of this paper is that semantic interoperability is a key value enabler for IoT and
that establishing a shared ontology based approach is critical for the development and exploitation
of the technology.
Foundations of Semantic Interoperability
Metadata, an essential data reusability provider
What is the issue we want to solve?
Interoperability is commonly driven by the respective parties sharing a priori knowledge of some
kind; for example, shared knowledge of an application programming interface (API) or shared
knowledge of a set of database tables and related access rules. Key to these approaches is
conformance with prior agreements and understandings.
These data-model based semantic interoperability approaches are the cornerstone of inter-
operability in many enterprises and industrial contexts. One of the challenges, however, is that when
new applications are introduced into the context, they also need prior knowledge of the
interoperability schemes, the API specifications, and the meaning and use of database tables.
In the context of the IoT, however, there has to be a way to create an interoperability context which
does not rely on prior knowledge. This is the issue we are trying to solve and metadata is a core part
of the solution.
What is metadata?
Metadata is about describing the contents and context of data to facilitate discovery, understanding
and (re)usability of that data. Hence the usual statement that metadata is data about data.
It is often the case, however, that the actual meaning of data can only be discovered by examining
the software that generates and processes the data. This bundling obfuscates the semantics of the
data with the result that third-party processors receiving data have no guidance on how the values
should be interpreted and understood.
Figure 2 Meaningfulness of the data, increased with metadata
Metadata is about reducing the separation between semantics and values by ensuring that data is
provided with context and description. This enables interpretation and understanding by subsequent
processors and provides foundational support for interoperability and reusability.
Figure 2 [7] provides a view on the metadata associated with a temperature sensor. As can be seen,
multiple levels of meaning can be inferred; this gives transparency to the context and supports
subsequent design-time abstraction and modeling views.
Sharing metadata
The Linked Open Vocabularies4 (LOV) community is driving a conversation around the creation and
use of shared metadata (shared vocabularies).
While locally defined and shared metadata will deliver value within a given domain, metadata which
is published more widely will necessarily drive interoperability and reusability to a greater extent, as
shown in Figure 3.
Programs such as the H2020 Large Scale Pilots [8] which address domain specific and cross-domain
concerns, as shown in Figure 4, provide multiple opportunities for exercising and proving the
strategic value of sharing metadata across a significant scope at scale.
What is an Ontology?
The LOV community is focusing on the curation of quality vocabularies across all domains.
Taxonomies often build on such controlled vocabularies using parent-child relationships to describe
the organization of terms within a specific domain. Ontologies extend this concept further to capture
relationships capable of supporting richer operations and more advanced levels of reasoning.
Ontologies build on metadata to provide a representation of knowledge about a given domain and
to provide a core resource for reasoning about a domain and a context. The Semantic Sensor
Network (SSN) Ontology [9] is an example of an existing ontology which describes the capabilities
and properties of sensors, the act of sensing and the resulting observations. Another example is the
oneM2M Base Ontology [10] that constitutes a framework for specifying the semantics of data that
are handled in oneM2M and to which domain specific ontologies can be mapped.
Figure 5 shows the key concepts and relations of the SSN ontology split by conceptual modules
(dotted lines).
Figure 6 shows the core concepts of the oneM2M Base Ontology.
Figure 4 Metadata and Data Reusability
. . .Smart
Core metadata used across application domains
Industry specific groups are in the best position to define metadata for each vertical
Horizontal and Vertical Metadata
Figure 3 Shared Metadata
based on
Ver y
Figure 5 Semantic Sensor Network Ontology
Figure 6 oneM2M Base Ontology
Ontologies and the IoT
Given the cross-domain nature of the IoT, there is a need both to capture and express knowledge
shared across the verticals and to leverage linkages between domains.
As can be seen in Figure 5, the SSN ontology comprises ten conceptual modules relating to sensors.
This modularity supports reuse of SSN concepts in other ontologies and, similarly, concepts from
other ontologies can be included into solutions using the SSN ontology as required.
In Figure 6 the core aspects of the oneM2M Base Ontology are shown. The ontology provides a
common, domain-independent basis to which existing domain-specific ontologies, e.g. SAREF [11] ,
can be mapped. IoT devices described according to the concepts of the Base Ontology, or derived
from concepts in domain-specific ontologies, can be automatically mapped to a REST resource
structure in oneM2M.
Modularity, reuse and linkage are key strategies for supporting the use of ontologies in building
cross-domain IoT applications. These strategies, plus the need to educate the community in their
existence and usage are discussed in [12] [13] .
The diversity of IoT domains will drive ontology development through vocabulary creation, extension,
reuse, and retargeting; this motivates requirements for ontology management capabilities. The
richness of semantic data models can be leveraged to automate management capabilities - such
automation will be crucial for the continued operation of IoT ecosystems in which human
intervention is expected to be minimal, ineffective and/or unavailable.
Ontologies and Modularity
Requirements for modularization are commonly driven by use-cases in which only parts of an
existing ontology are needed, or in which constrained devices are unable to perform inference and
reasoning on a full ontology. Modularization also eases some of the complexities around semantic
data modelling and ontology design, integration, maintenance, and reuse [14] .
Modularization requires the partitioning of ontologies into independent sub-modules [15] [16] . Sub-
modules are self-contained knowledge components that:
Are loosely coupled
Define their own set of core concepts and relations
Are reusable
Are linked to other module(s) with explicit relationship(s).
As a consequence of the loose coupling, modules can be designed, used, managed and updated in a
stand-alone manner, with no impact on other modules. When modularizing ontologies, however, it’s
also important to avoid generating reasoning or querying complexities for future (module) unions.
Good examples of modular ontologies are the Smart BANs (Body Area Networks) and MyOntoSens
ontologies [17] [18] . Within MyOntoSens, a Wireless Sensor Network (WSN) module is formed of
clusters (Cluster module; BAN module for Smart BANs) that are composed of nodes (Node Module).
A node is used for process (Process Module) and takes measurements (Measurements Module). The
‘Measurements Module’ is sufficiently light to be instantiated and stored within sensors, while the
Process and Measurements modules full instantiation and inference/reasoning can actually only be
performed within a more capable node, the cluster sink (or BAN hub). The full BAN ontology
(including service level modules), is instantiated, inferred and processed within remote and
distributed monitoring and control servers (e.g. hospital servers).
Ontology modularity can also be handled using a layered approach as shown in Figure 7. Although
heterogeneity characterizes the landscape of devices and systems across domains, there are
commonalties which can be abstracted out. Thus, ontologies can often be modularized into at least
two layers: cross-domain ontologies and domain ontologies.
The cross-domain ontologies consist of concepts shared across domains and silos. For instance, a
general protocol ontology can be used to classify the communication protocols along with
information regarding the supported communication medium and range. Such general information
can be used during diagnostic and maintenance operations. Similarly, there can be multiple cross-
domain ontologies covering shared concepts related to quantities, units, topological relations,
location, and usage.
The cross-domain ontologies capture the shared concepts across domains and constitute the
building blocks of future extensions.
The domain ontologies relate to specific silos or verticals and often reference the cross-domain
ontologies. For example, in Figure 7 the Buildings Ontology relies on both the Physical Quantities
Ontology to express the measurements, and on the Localization Ontology to reference a site or a
floor. Moreover, both cross-domain and domain ontologies can also rely on existing dictionaries
(such as HayStack5 from the building domain).
Figure 7 Multi-layered Ontologies
Consider the concepts of Current and Phase in the Buildings context:
The Quantities Ontology (cross-domain) includes definitions for Current and Phase and also
defines the hasPhase relation. Now assume that A is an instance of Phase.
The Buildings Ontology (vertical domain) has a similar model and represents a current of
phase A as IA. Thus, using the cross-domain ontology, the IA definition can be expressed as
follows: IA ≡ Current and hasPhase A.
Similarly, the Energy Ontology refers to a current of phase A as CA, then its definition (by
reuse of the cross-domain concepts) becomes CA ≡ Current and hasPhase A.
A multi-layer approach enables great flexibility when querying since the ontologies are
interconnected and queries can exploit both high level and specific concepts to explore a domain.
Applications operating at a high-level of abstraction can use more general concepts to retrieve
information such as (Current and hasPhase A), while applications operating at a more granular level
can rely on the vertical ontologies to formulate queries and extract specific information such as CA
and IA.
Taking a further example, oneM2M provides a set of rules to map the conceptual model of the
oneM2M Base Ontology to the underlying oneM2M resource structure. Then for systems using
other ontologies for which a mapping to the oneM2M Base Ontology can be defined, this provides a
mechanism for those (other) ontologies to be instantiated within a oneM2M system as resources
with associated semantic annotation. This enables different IoT systems to interwork with each
other via a common upper ontology (Base Ontology) and a common architecture.
Ontologies and Semantically Augmented Things
Designing ontologies is the first step towards the interoperability vision; the second step consists of
enabling the sensors, devices and systems to express their contextual information and data by
applying the ontologies.
The connected world is a diverse ecosystem comprising elements which range from small,
constrained devices and sensors, to larger more complex modules and machinery. This diversity is
reflected in the processing, storage and communication capabilities provided by the respective
elements and it follows that the degree to which ontologies and semantic capabilities can be
embedded will also vary.
These considerations impact constrained elements for which embedding semantics is not an option.
Sparsely resourced sensors, for example, will often provide little in terms of processing and may only
support very lightweight, near binary format communications. In these cases, metadata can be
(externally) attached to the sensor’s data in a process referred to as semantic annotation [19] [20] .
Semantic annotation is usually performed by the agent receiving the sensor’s data, for example a
gateway, a system, or a cloud agent [20] [21] .
We distinguish between two semantic annotation mechanisms; automatic tagging and
commissioning [19] .
Automatic Tagging can be handled by a software agent running on a gateway, on a system or in the
cloud. The agent decodes the sensor data stream (for example) and then augments the data using an
appropriate semantic representation - see Figure 8. Other approaches, such as in [22] , suggest a
heuristic based inference to harmonize the tags based on previously existing unstructured data.
Figure 8 Semantic Annotation
Commissioning is usually handled through a user interface during the installation phase of a gateway
or a system. For example, the use and location of a given sensor are only known during the
commissioning phase and at that time the installer uses a commissioning tool to set the data from
the ontologies. Commissioning tools should evolve to take into account such tagging.
Depending on the resources of the gateway or system, such annotation can remain as tags which
can be processed by query engines to answer specific queries, as in [19] . Other approaches can rely
on such tags to generate a complete ontology, as in [20] [21] .
Ontologies and Context
IoT technology is driving new opportunities for context-aware systems and applications. These
classes of sentient system and application are able to adapt their behavior to the current context
without explicit intervention.
Context awareness often means that a system combines physical awareness (time, location, sound,
movement, touch, temperature) with application awareness (tasks, goals, processes, compliance,
compatibility, approval, user disposition) to modify its own behavior.
Metadata alone has proven insufficient to address interoperability; in some institutions it is not part
of software engineering curricula. Due to their formal expressiveness and the possibilities for
applying ontology reasoning techniques, various existing and emerging context-aware frameworks
use ontologies in their implementation [23] . Context sensitive machine learning techniques are also
finding a role in deriving interoperability contexts [24] .
Ontologies and Scalability
Figure 9 Scalability of ontology-based integration
A growing number of devices and applications
are delivering data-streams and events on a
continuous basis. This growth in data volume
and velocity is accompanied by a growth in
variety, driven by the heterogeneity of device
and data formats.
Data integration programs based on
traditional approaches such as relational
databases are efficient in small, static and
closed environments. In fact, with a low
number of data sources, the cost of using and
maintaining data remains low compared to an
ontology-driven approach where a larger
initial amount of effort is required therefore
involving high costs.
The benefit of semantic standards stands out, however, when it comes to large, dynamic and open
systems with critical requirements in terms of scalability and interoperability. Semantic models
enable integrating a huge number of heterogeneous and mobile sources in short period of time with
reasonable costs compared to traditional approaches. Semantic integration is performed once at the
beginning, paving the way for advanced querying and reasoning, and enabling data to be integrated
in a collaborative, standard and reusable way.
Current and Emerging Practices
Technologies and Strategies for Linked Data
The key to overcoming the fragmentation of the IoT and catalyzing exponential growth in services
will be enabling end-to-end interoperability across different platforms. This requires open standards
for metadata that define the data and interaction models exposed to applications, the protocols
involved, and the communication patterns that can be used. In other words, this requires standards
for a web of things.
Source: PricewaterhouseCoopers, 2009 [25]
What are the technologies and strategies for handling such metadata?
The Resource Description Framework (RDF) [26] provides for globally unique identifiers for metadata.
These identifiers in many cases serve as links to further information for a web of linked data. RDF
allows data and metadata to be described in terms of triples, i.e. named relationships that connect a
subject to an object. There are multiple serialization formats for RDF, e.g. RDF-XML [27] , Turtle [28]
and N3 [29] , comma separated values [30] , and JSON-LD [31] . Further techniques address how to
include metadata within web pages.
Semantic models can be expressed with RDF Schema (RDF-S) [32] or the Web Ontology Language
(OWL) [33] . SPARQL [34] is a query language for accessing and updating RDF triples. The Linked
Data Platform (LDP) [35] defines how to use HTTP for read-write linked data on the web. DCAT [36]
is an RDF vocabulary designed to facilitate interoperability between data catalogs published on the
Web. Linked Open Vocabularies (LOV) community, introduced in the “Sharing Metadata” section,
maintains descriptions of RDF-S vocabularies and OWL ontologies used for datasets in the Linked
Data Cloud, see [37] .
Semantics in Support of Cross-Domain IoT
Semantic technologies provide a common means to describe domain knowledge whilst enabling
heterogeneity and multimodality through interoperable data formats and various semantic models
[38] .
Beyond the representational aspects, semantic computing supports reasoning on raw sensor data
enabling the derivation of higher level abstractions; such abstractions form the basis of domain- and
cross-domain knowledge. The Machine-to-Machine Measure (M3) Framework discusses these
concepts [41] and provides an implementation which explores the creation of cross-domain
Figure 10, which is discussed in [42] , outlines how M3 applies a semantic approach to enable cross-
domain reasoning. The figure shows two sensors in different domains; sensor A is in the health
domain and sensor B is in the weather domain.
Designing Semantic Models
Semantic data model design can be mainly split into two phases:
1. Specification, i.e. the conceptual/logical/abstract definition of the model. This is mainly
mapped out in the form of objects, materialized as classes that can be linked together
(relationships) and that are described by attributes
2. Formalization, i.e. its physical model in the form of semantic metadata or ontology.
Conceptual models are generally structured through the use of Entity Relationship (ER) or Unified
Modeling Language (UML) diagrams. In line with practices commonly followed in Semantic Web [43]
and RDF [44] development, Java conventions [45] are generally observed. For example:
Adopt naming conventions which impose minimum changes
Replace spaces in strings with underscores
Use lower-case for metadata and ontologies namespaces
Use camel-case for class and object names
Use mixed-case for property names
The formalization of a semantic data model is achieved through the use of description languages.
Lightweight description languages such as JSON-LD (JavaScript Object Notation for Linked Data) [46]
are generally preferred when dealing with low-power, low-energy, constrained embedded devices
such as sensors and actuators. Less constrained environments commonly use XML based description
languages such as OWL [47] . Best practices are available through the Semantic Web and Linked Data
initiatives [48] [49] .
As discussed earlier, an ability to derive cross-domain value in an IoT context requires an ability to
leverage multiple ontologies. Leveraging implies addressing functions such as ontology publication,
discovery, reuse and mapping. Best practices for ontology engineering are available, for example
[50] , but there are no de facto or standard references.
Step 1:
The raw measurements generated by the sensors are transformed into metadata which contain additional
Unit of Measurement
Software Version
Domain of Operation
Step 2:
The framework encodes the metadata using Sensor Markup Language [39] before converting into RDF [40]
to enable semantic reasoning.
Step 3:
Semantic reasoning drives higher level abstractions as new domain concepts. In the health domain the
reasoning leads to the concept of “flu”; in the weather domain to the concept of “hot”.
Step 4:
The respective domain ontologies are used to classify these new concepts; “flu” as a disease and “hot” as a
seasonal condition.
Step 5-7:
The concepts, rules and datasets of the two domains are combined and cross-domain semantic reasoning
takes place. In this example, the cross-domain reasoning produces suggestions for recipes appropriate for a
given state of health and the prevailing weather conditions. The suggestions can be acted upon both by end-
users and intelligent machines.
Figure 10 Enabling cross-domain scenarios using the M3 framework
The Emergence of API First and Microservices
Many of the major cloud service and software providers are giving attention to API First [51]
application development approaches and serverless computing architectures [52] . Built on both in-
house technologies and technologies from innovative startups these strategies target much of the
heavy lifting in terms of hardware and software infrastructure (that includes provisioning for
availability, throughput management, runtime analytics, etc.) enabling developers to focus the
majority of their attention on the specific functionality of their respective products.
A further trend is the growing application of the microservices architectural style "an approach to
developing a single application as a suite of small services, each running in its own process and
communicating with lightweight mechanisms, often an HTTP resource API" [53] .
API First, serverless computing and microservices are being driven by the growing penetration of
smart devices (in homes, cars, buildings, etc.), the ubiquity of smart phones and tablets as powerful
mobile computing platforms, and the pressure to deliver the methods, tools and infrastructure to
enable rapid realization of sophisticated, secure and scalable cloud-based IoT applications. The
framework-oriented Ontology as a Service [54] or microservices conforming to the SSN-based IoT-
Lite [55] are illustrative of these approaches.
Given the inherent cross-domain nature of IoT, it’s likely that these forces will combine to accelerate
the move towards semantic interoperability.
Conclusion and Closing Position
Although both the deployment of semantic technologies and the availability of skills are at fairly
embryonic stages, there is a growing understanding that shared approaches to semantic
interoperability is one of the keys to unlocking the value potential of the IoT. Indeed, this paper,
which results from the efforts of a broad range of individuals responding to calls within the AIOTI
WG3, oneM2M, IEEE P2413 and W3C communities, underlines that fact.
Realizing semantic interoperability at scale will require collaboration and coordination across
standards organizations, consortia, alliances, and open source projects. The need for a shared
roadmap and commitment to work together seems self-evident.
An initial focus may be created around lightweight models of semantics sufficient to manage
interoperability of common domain independent and domain specific terms. There is increasing
interest in providing agile processes for standardizing such terms and defining accessible, usable
schemes for discovery and reuse. Such schemes will need to address different stages of lifecycle and
maturity, e.g. from experimental, to commercial implementations, to (eventual) deployment on a
global scale. An interesting precedent is provided by which defines a widely used
lightweight RDF compatible vocabulary for websites to describe themselves to search engines.
Could this be generalized to descriptions of IoT services?
We challenge the community to initiate a process of alignment, consolidation and focus around
semantic interoperability by:
Establishing focused collaborations
Creating a shared roadmap for addressing semantic interoperability concerns
Adopting the roadmap to identify priorities, inform programs and rationalize deliverables
We look to standards organizations, consortia, alliances, and open source projects to endorse this
approach and to proactively move the agenda forward.
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... • C5: Standardization-compliancy: Lessons learned from semantic interoperability are disseminated within the ISO/IEC 21823-3 IoT semantic interoperability [16], and the Alliance for the Internet of Things Innovation (AIOTI) Standardization WG (, accessed on 17 September 2022), which includes the Semantic Interoperability Expert Group [17,18] where the rule-based inference engine is taken as a baseline [19]. SAREF designers are also members of AIOTI Standard WG. ...
... The ontology chosen must be compliant with a set of rules to infer additional information. The Reasoning Engine API (inspired from [55,87] and supported by the AIOTI group on semantic interoperability [19]) deduces additional knowledge from the data (e.g., abnormal heartbeat) using rule-based reasoning. The IF THEN ELSE rules executed by the reasoning engine will add new data in the data storage. ...
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Humans are feeling emotions every day, but they can still encounter difficulties understanding them. To better understand emotions, we integrated interdisciplinary knowledge about emotions from various domains such as neurosciences (e.g., neurobiology), physiology, and psychology (affective sciences, positive psychology, cognitive psychology, psychophysiology, neuropsychology, etc.). To organize the knowledge, we employ technologies such as Artificial Intelligence with Knowledge Graphs and Semantic Reasoning. Furthermore, Internet of Things (IoT) technologies can help to acquire physiological data knowledge. The goal of this paper is to aggregate the interdisciplinary knowledge and implement it within the Emotional Knowledge Graph (EmoKG). The Emotional Knowledge Graph is used within our naturopathy recommender system that suggests food to boost emotion (e.g., chocolate contains magnesium that is recommended when we feel depressed). The recommender system also answers a set of competency questions to easily retrieve emotional related-knowledge from EmoKG, such as what are the basic emotions and the more sophisticated ones, what are the neurotransmitters and hormones related to emotions, etc. To follow FAIR principles, EmoKG is mapped to existing knowledge bases found on the BioPortal biomedical ontology catalog such as SNOMEDCT, FMA, RXNORM, MedDRA, and also from emotion ontologies (when available online). We design the LOV4IoT-Emotion ontology catalog that encourages researchers from heterogeneous communities to apply FAIR principles by releasing online their (emotion) ontologies, datasets, rules, etc. The set of ontology codes shared online can be semi-automatically processed; if not available, the scientific publications describing the emotion ontologies are semi-automatically processed with Natural Language Processing (NLP) technologies. This research is also relevant for other use cases such as European projects (ACCRA for emotional robots to reduce the social isolation of aging people, StandICT for standardization, and AI4EU for Artificial Intelligence) and alliances for IoT such as AIOTI. The recommender system can be extended to address other advice such as aromatherapy and take into consideration medical devices to monitor patients’ vital signals related to emotions and mental health.
... The Web of Things (WoT) Initiative has presented a study of the advantages and challenges of open systems with an example of a smart pilot system for managing a city's climate and improving interoperability with devices, where WoT is a platform that allows users to interact with IoT via the web with open standard technologies, with an example of some of the most famous interoperability platforms (Ready4SmartCity, OpenSensingCity, Lov, Lov4IoT) [55], [73]. Federation, discussed in [40], aims to achieve integration by supporting unified interfaces, agreed standards for data models, design engineering constraints, and specific standard communication messages between parts and collaborating systems. ...
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Interoperability is a functionality that facilitates integration amongst disparate devices and systems used by applications. Integration, inter-operability, middleware, and standardization are some of the synonyms or solutions of interoperability. As such, interoperability facilitates timely, efficient, and effective completion of applications, in addition to finding new, smarter, and more adaptive services. Smart cities, like many other environments and applications, suffer from the lack of interoperability, which makes their processing very challenging. The lack of interoperability also leads to ineffectiveness, which is highly undesirable for applications that deal with emergencies or have exceptional requirements. In particular, interoperability is highly desirable in heterogeneous systems. This research presents a comprehensive review of the available methods and ways to deal with the issues related to interoperability. In addition, the article provides a classification of the available solutions to overcome the lack of interoperability. Various methods which claim to provide interoperability, are sorted out according to the domains and context in which they appear. This research has identified the advantages and limitations of the available methods for facilitating interoperability. A comprehensive framework for dealing with Interoperability in different domains is proposed. This framework provides a hybrid approach for dealing with interoperability, which could be regarded as a comprehensive and reliable solution when dealing with smart cities.
... Semantic interoperability for the Web of Things was surveyed in [248] with the conclusion, that deployment of semantic technology is still at the beginning and to achieve semantic interoperability at scale, requires collaboration across standards organizations, consortia, alliances, and open source projects. Therefore many organizations work on semantics for IoT. ...
The Internet of Things (IoT) is a system of physical objects that can be discovered, monitored, controlled, or interacted with by electronic devices that communicate over various networking interfaces and eventually can be connected to the wider Internet. [Guinard and Trifa, 2016]. IoT devices are equipped with sensors and/or actuators and may be constrained in terms of memory, computational power, network bandwidth, and energy. Interoperability can help to manage such heterogeneous devices. Interoperability is the ability of different types of systems to work together smoothly. There are four levels of interoperability: physical, network and transport, integration, and data. The data interoperability is subdivided into syntactic and semantic data. Semantic data describes the meaning of data and the common understanding of vocabulary e.g. with the help of dictionaries, taxonomies, ontologies. To achieve interoperability, semantic interoperability is necessary. Many organizations and companies are working on standards and solutions for interoperability in the IoT. However, the commercial solutions produce a vendor lock-in. They focus on centralized approaches such as cloud-based solutions. This thesis proposes a decentralized approach namely Edge Computing. Edge Computing is based on the concepts of mesh networking and distributed processing. This approach has an advantage that information collection and processing are placed closer to the sources of this information. The goals are to reduce traffic, latency, and to be robust against a lossy or failed Internet connection. We see management of IoT devices from the network configuration management perspective. This thesis proposes a framework for network configuration management of heterogeneous, constrained IoT devices by using semantic descriptions for interoperability. The MYNO framework is an acronym for MQTT, YANG, NETCONF and Ontology. The NETCONF protocol is the IETF standard for network configuration management. The MQTT protocol is the de-facto standard in the IoT. We picked up the idea of the NETCONF-MQTT bridge, originally proposed by Scheffler and Bonneß[2017], and extended it with semantic device descriptions. These device descriptions provide a description of the device capabilities. They are based on the oneM2M Base ontology and formalized by the Semantic Web Standards. The novel approach is using a ontology-based device description directly on a constrained device in combination with the MQTT protocol. The bridge was extended in order to query such descriptions. Using a semantic annotation, we achieved that the device capabilities are self-descriptive, machine readable and re-usable. The concept of a Virtual Device was introduced and implemented, based on semantic device descriptions. A Virtual Device aggregates the capabilities of all devices at the edge network and contributes therefore to the scalability. Thus, it is possible to control all devices via a single RPC call. The model-driven NETCONF Web-Client is generated automatically from this YANG model which is generated by the bridge based on the semantic device description. The Web-Client provides a user-friendly interface, offers RPC calls and displays sensor values. We demonstrate the feasibility of this approach in different use cases: sensor and actuator scenarios, as well as event configuration and triggering. The semantic approach results in increased memory overhead. Therefore, we evaluated CBOR and RDF HDT for optimization of ontology-based device descriptions for use on constrained devices. The evaluation shows that CBOR is not suitable for long strings and RDF HDT is a promising candidate but is still a W3C Member Submission. Finally, we used an optimized JSON-LD format for the syntax of the device descriptions. One of the security tasks of network management is the distribution of firmware updates. The MYNO Update Protocol (MUP) was developed and evaluated on constrained devices CC2538dk and 6LoWPAN. The MYNO update process is focused on freshness and authenticity of the firmware. The evaluation shows that it is challenging but feasible to bring the firmware updates to constrained devices using MQTT. As a new requirement for the next MQTT version, we propose to add a slicing feature for the better support of constrained devices. The MQTT broker should slice data to the maximum packet size specified by the device and transfer it slice-by-slice. For the performance and scalability evaluation of MYNO framework, we setup the High Precision Agriculture demonstrator with 10 ESP-32 NodeMCU boards at the edge of the network. The ESP-32 NodeMCU boards, connected by WLAN, were equipped with six sensors and two actuators. The performance evaluation shows that the processing of ontology-based descriptions on a Raspberry Pi 3B with the RDFLib is a challenging task regarding computational power. Nevertheless, it is feasible because it must be done only once per device during the discovery process. The MYNO framework was tested with heterogeneous devices such as CC2538dk from Texas Instruments, Arduino Yún Rev 3, and ESP-32 NodeMCU, and IP-based networks such as 6LoWPAN and WLAN. Summarizing, with the MYNO framework we could show that the semantic approach on constrained devices is feasible in the IoT.
... IoT suffers from a lack of semantic interoperability between heterogeneous devices. These challenges are highlighted in [7]. The European Research Cluster on the IoT released IoT semantic interoperability best practices and recommendations [15], but does not refer concrete tools to encourage the reuse of the domain knowledge already designed. ...
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One of the main challenges in the Internet of Things (IoT) is the lack of semantic interoperability between heterogeneous sources. In the Semantic Web domain, ontologies are one way to achieve semantic interoperability by using a common vocabulary that represents heterogeneous sources. However, recent studies have shown that the amount of concept reuse from existing IoT ontologies is low. As the number of IoT ontologies increases, encouraging users to reuse existing ontologies instead of creating new concepts becomes important. Ontology catalogues are a prominent approach to discover and inspect existing ontologies for reuse. However, such catalogues inspect the ontologies using general criteria which is not enough to understand the content of the ontology. In this paper, we propose a method for automatic ontology inspection (OntoSpect) of IoT ontologies from different application domains based on a generic set of content-related concepts. OntoSpect consists of two main steps: first it extracts the set of IoT concepts, and then generates human-understandable descriptions using a Model-driven Engineering (MDE) approach. We evaluate the quality of concept extraction and natural language description generation with 84 ontologies retrieved from the LOV4IoT catalogue and report on quality metrics. In addition, we conduct an empirical study with 28 ontology users to further assess the quality of the generated descriptions. The results demonstrate the capability of OntoSpect to support ontology users inspecting IoT ontologies.
... We are not aware of any comparable approaches that integrate IoT sensors in a semi-automatic way. This paper seeks to address the general problem of semantic interoperability [13] using Semantic Web approaches as demonstrated by many semantic IoT systems such as OpenIoT 1 . However, existing systems do not generally follow the idea of an IoT search engine and leave the semantic annotation process to integrators. ...
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Sensor deployments in Smart Homes have long reached commercial relevance for applications such as home automation, home safety or energy consumption awareness and reduction. Nevertheless, due to the heterogeneity of sensor devices and gateways, data integration is still a costly and timeconsuming process. In this paper we propose the Smart Home Crawler Framework that (1) provides a common semantic abstraction from the underlying sensor and gateway technologies, and (2) accelerates the integration of new devices by applying machine learning techniques for linking discovered devices to a semantic data model. We present a first prototype that was demonstrated at ICT 2018. The prototype was built as a domainspecific crawling component for IoTCrawler, a secure and privacy-preserving search engine for the Internet of Things.
Conference Paper
Objectives/Scope Drilling operations rely on the collaboration of many participants, and the efficiency of this collaboration depends on timely exchange of information. The complexity and variability of this information make it difficult to achieve interoperability between the involved systems. Recent industry efforts aim at facilitating the many aspects of interoperability. A central element is semantic interoperability: the ability to correctly interpret the real-time signals available on the rig. This contribution presents an implementation of semantic interoperability using OPC UA technology. It translates the principles developed through joint industry efforts into actual drilling operations. Methods, Procedures, Process The process used the steps of characterizing the drilling real-time data with semantic graphs, and then developing methods to transfer this characterization to an operational real-time environment. A semantic interoperability API (application programming interface) uses the semantic modelling capabilities of OPC UA. Its objectives are to facilitate the acquisition and identification of real-time signals (for data consumers) and their precise description (by data providers). The different components of the API reflect the diversity of scenarios one can expect to encounter on a rig: from WITS-like data streams with minimal semantics to fully characterized signals. The high-level interface makes use of semantical techniques, such as reasoning, to enable advanced features like validation or graph queries. Results, Observations, Conclusions The implementation phase resulted in a series of open-source solutions that cover all the stages of semantic interoperability. The server part integrates real-time sources and exposes their semantics. Data providers can use dedicated applications to accurately describe their own data, while data consumers have access to both predefined mechanisms and to more advanced programming interfaces to identify and interpret the available signals. To facilitate the adoption of this technology, test applications are available that allow interested users to experiment and validate their own interfaces against realistic drilling data. Finally, demonstrations involving several participants took place. The paper discusses both the testing procedures, the results and insights gained. Novel/Additive Information The solutions described in this contribution build on newly developed interoperability strategies: they make on-going industry efforts available to the community via modern technologies, such as OPC UA, semantic modelling, or reasoning. Our hope is that the adoption of the developed technology should greatly facilitate the deployment of next generation drilling automation systems.
Background: Digital Twins are becoming ubiquitous in a range of domains, such as construction or e-health. Due to the numerous applications where Digital Twins can be adopted, they have to manage heterogeneous cross-domain data that is consumed by third-party service or domain experts. Digital Twins may allow for performing operational commands that are transformed into actions in the physical world; these operations can be grouped into processes. In the domain of construction, a European Project named COGITO was proposed, in which a Digital Twin is used to simulate the construction processes carried out in three railway stations in Europe (Spain, Denmark and Austria). The lack of interoperable data due to heterogeneity in the APIs of the physical layer, the complexity that entails managing these data at the digital model, and the difficulty of orchestrating the data exchanged among the advanced features have been the roadblocks in the project. Methods: The problems have been addressed by adopting the W3C Web of Things (WoT) standard, by using Thing Descriptions. Although the challenges are specific for the COGITO project, they could be found in any Digital Twin proposal. Thus, the article presents how they are solved and addressed in COGITO. Results: The results demonstrate that relying on a Knowledge Graph for the provenance, another for describing a distributed architecture of semantic interoperability services and a final one for data, allows to build a distributed Digital Twin over heterogeneous data sources that provide semantic interoperable data in each digital layer. Conclusions: The lack of a Digital Twin standard highlights the necessity of adopting existing standards and extending them in order to adapt their suitability to the Digital Twin context. Hence, the article presents how WoT is helpful for addressing the challenges, and how it could be extended in the future to support Digital Twins.
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Social companion robots are getting more attention to assist elderly people to stay independent at home and to decrease their social isolation. When developing solutions, one remaining challenge is to design the right applications that are usable by elderly people. For this purpose, co-creation methodologies involving multiple stakeholders and a multidisciplinary researcher team (e.g., elderly people, medical professionals, and computer scientists such as roboticists or IoT engineers) are designed within the ACCRA (Agile Co-Creation of Robots for Ageing) project. This paper will address this research question: How can Internet of Robotic Things (IoRT) technology and co-creation methodologies help to design emotional-based robotic applications? This is supported by the ACCRA project that develops advanced social robots to support active and healthy ageing, co-created by various stakeholders such as ageing people and physicians. We demonstra this with three robots, Buddy, ASTRO, and RoboHon, used for daily life, mobility, and conversation. The three robots understand and convey emotions in real-time using the Internet of Things and Artificial Intelligence technologies (e.g., knowledge-based reasoning).
In this chapter, we propose to investsigate the applicability of semantics in the context of Internet-of-Things (IoT) to trace the origins of medical data. As IoT-devices have become the first-order source of information in the field of healthcare in various systems, the challenge of correctness and reliability of retrieved data is becoming of tremendous importance. This challenge is directly connected with the quality of patient monitoring and treatment because the decision on the patient’s state is made according to the set of measured parameters. Inaccuracy and low quality of measurements that may be caused by sensor malfunction, incorrect measurement procedure, etc. can lead to problems with comprehension of the current situation and affect further decisions. The photometric calibrating curves of Melatonin-sulfate in human urine were considered as a case-study. The Hill’s equation was used for ‘dose–response’ relationship. The photometric calibrating graphs of Melatonin-sulfate in human urine were considered as a case study. Hill’s equation imaged the ‘dose–response’ relation. The photometric transmittance of analyzed solutions was the response signal. The ordinary photometry of human urine can be in use as the simple ex-press-analysis of melatonin instead of expensive analyzes. If, sure, the accord-ant calibrators are reliable. The existing set of such calibrators yet unable warrants the trusty calibrating. Thus, the medical photometry of urinary Melatonin-sulfate is yet out of extensive use. The problem of reliable calibrators is mostly in the provenance of data.
Conference Paper
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The Internet-of-Things paradigm has brought exciting opportunities to increase productivity and efficiency but also customer experiences. Thus, IoT promoted both the interconnectivity of devices and systems but also their cloud connectivity. However, such plethora of connected things capture part of the contextual information in different data models which makes them operate in silos. We depict in this paper an architecture and a real experiment to connect our on-premise systems to the cloud along with a semantic representation of the contextual information. Then, we integrate a BI component exposing data from our several systems to drive better insights in our facility.
Conference Paper
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The Advent of Internet-of-Things (IoT) paradigm has brought exciting opportunities to solve many real-world problems. IoT in industries is poised to play an important role not only to increase productivity and efficiency but also to improve customer experiences. Two main challenges that are of particular interest to industry include: handling device heterogeneity and getting contextual information to make informed decisions. These challenges can be addressed by IoT along with proven technologies like the Semantic Web. In this paper, we present our work, SQenIoT: a Semantic Query Engine for Industrial IoT. SQenIoT resides on a commercial product and offers query capabilities to retrieve information regarding the connected things in a given facility. We also propose a things query language, targeted for resource-constrained gateways and non-technical personnel such as facility managers. Two other contributions include multi-level ontologies and mechanisms for semantic tagging in our commercial products. The implementation details of SQenIoT and its performance results are also presented.
Conference Paper
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Building automation systems are believed to hold the key to significantly reducing the average energy consumption of our residential and commercial building stock, which in the U.S. is responsible for 41% of the total annual energy use in 2014. As these systems become more widespread and inexpensive, the complexity and challenges associated with their installation, maintenance and upkeep will increase. One of the primary challenges is the generation and update of the meta-data associated with the sensors and control points distributed throughout the facility. Previous research has attempted to reduce the human input required to perform these activities, by leveraging different signal processing and statistical analysis approaches to infer the sensor types and locations from measurements and/or tags obtained through a BAS. However, because of the relatively small sample size, the feasibility of applying these type approaches on large buildings, as well as their generalizability, remain as unsolved questions. In this paper, we propose a meta-data inference framework to learn from BAS measurement data in a semi-automated way. Furthermore, we evaluate the framework on two large buildings instrumented with thousands sensors and show the feasibility of applying data driven approaches in the real world. We present the results of our study and provide recommendations for future work in this area.
Conference Paper
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The Internet of Things, more specifically, the Machine-to-Machine (M2M) standard enables machines and devices such as sensors to communicate with each other without human intervention. The M2M devices provide a great deal of M2M data, mainly used for specific M2M applications such as weather forecasting, healthcare or building automation. Existing applications are domain-specific and use their own descriptions of devices and measurements. A major challenge is to combine M2M data provided by these heterogeneous domains and by different projects. It is really a difficult task to understand the meaning of the M2M data to later reason about them. We propose a semantic-based approach to automatically combine, enrich and reason about M2M data to provide promising cross-domain M2M applications. A proof-of-concept to validate our approach is published online (
This volume explores how context has been and can be used in computing to model human behaviors, actions and communications as well as to manage data and knowledge. It addresses context management and exploitation of context for sharing experience across domains. The book serves as a user-centric guide for readers wishing to develop context-based applications, as well as an intellectual reference on the concept of context. It provides a broad yet deep treatment of context in computing and related areas that depend heavily on computing. The coverage is broad because of its cross-disciplinary nature but treats topics at a sufficient depth to permit a reader to implement context in his/her computational endeavors. The volume addresses how context can be integrated in software and systems and how it can be used in a computing environment. Furthermore, the use of context to represent the human dimension, individually as well as collectively is explained. Contributions also include descriptions of how context has been represented in formal as well as non-formal, structured approaches. The last section describes several human behavior representation paradigms based on the concept of context as its central representational element. The depth and breadth of this content is certain to provide useful as well as intellectually enriching information to readers of diverse backgrounds who have an interest in or are intrigued by using context to assist in their representation of the real world.