=Paper=
{{Paper
|id=Vol-1783/paper-06
|storemode=property
|title=Introducing Thing Descriptions and Interactions: An Ontology for the Web of Things
|pdfUrl=https://ceur-ws.org/Vol-1783/paper-06.pdf
|volume=Vol-1783
|authors=Victor Charpenay,Sebastian Käbisch,Harald Kosch
|dblpUrl=https://dblp.org/rec/conf/semweb/CharpenayKK16
}}
==Introducing Thing Descriptions and Interactions: An Ontology for the Web of Things==
https://ceur-ws.org/Vol-1783/paper-06.pdf
Introducing Thing Descriptions and Interactions:
An Ontology for the Web of Things
Victor Charpenay1,2 , Sebastian Käbisch1 , and Harald Kosch2
1
Siemens AG — Corporate Technology
victor.charpenay@siemens.com, sebastian.kaebisch@siemens.com
2
Universität Passau — Fakultät für Informatik und Mathematik
harald.kosch@uni-passau.de
Abstract. The Internet of Things (IoT) and the Web are closely related
to each other. On the one hand, the Semantic Web has been including
vocabularies and semantic models for the Internet of Things. On the
other hand, the so-called Web of Things (WoT) advocates architectures
relying on established Web technologies and RESTful interfaces for the
IoT.
In this paper, we present a vocabulary for WoT that aims at defining
IoT concepts using terms from the Web. Notably, it includes two con-
cepts identified as the core WoT resources: Thing Description (TD) and
Interaction, that have been first elaborated by the W3C interest group
for WoT.
Our proposal is built upon the ontological pattern Identifier, Resource,
Entity (IRE) that was originally designed for the Semantic Web. To bet-
ter analyze the alignments our proposal allows, we reviewed existing IoT
models as a vocabulary graph, complying with the approach of Linked
Open Vocabularies (LOV).
Keywords: Web of Things, ontology, Semantic Web, Linked Open Vo-
cabularies
1 Introduction
Since the beginning, semantics have been a constituent part of the Internet
of Things (IoT). As it is envisioned that the number of devices and software
agents on the Internet will grow exponentially, domain models and ontologies
will likely play an important role to allow for more automation in controlling
and maintaining them [2]. As a consequence, proposals have emerged to integrate
concepts from the IoT into the Semantic Web and benefit from alignments with
existing vocabularies [16, 3, 8, 12]. The most successful example is probably the
Semantic Sensor Network (SSN) ontology [4].
In the mean time, the idea of a so-called Web of Things (WoT), that consists
in using the Web as an interoperable platform for IoT data, has grown a large
community and found adoption by the industry [7, 17]. The approach of the
Web of Things is resource-centric. It advocates the use of RESTful interfaces
2 Victor Charpenay et al.
to expose sensor data and controls. Although vocabularies and ontologies for
the IoT often offer a means to semantically describe the Web services of an
IoT device, none materializes the concept of resource. For a given URI, there
is no way to distinguish WoT resources (that interact with the physical world)
from more generic Web resources, such as personal Web pages or pages from
e-commerce platforms (that are either static documents or interfaces to other
digital systems). This limitation weakens interoperability and prevents truly
automated Web agents from interacting with the Web of Things.
Since 2014, a W3C interest group dedicated to the Web of Things has been
actively working on that topic3 . Among others, it has developed the notions of
Thing Description and Interaction, as the core WoT resources. As part of this
interest group, we have got interested in formally defining these two concepts in
the Semantic Web. With this work, we pursue two objectives: first, alignment
with other IoT vocabularies and, to a lesser extent, the capture of a common
understanding throughout the discussions within the group.
As alignment with existing IoT vocabularies is our main objective, we start
our work by reviewing vocabularies for the IoT. More precisely, we seek to an-
alyze the relations between them, i.e. the graph they form. This is presented in
the next section. We then present our proposal for a WoT ontology in Section 3,
including Thing Descriptions and Interactions. In section 4, although we do not
detail strict alignments, we show how to integrate our ontology to the graph of
IoT vocabularies. We then conclude.
2 Related Work
2.1 Ontologies for the Internet of Things
In order to be interoperable, IoT devices need to be able to share knowledge
about their capabilities or their environment. To that end, Semantic Web tech-
nologies and more precisely RDFS and OWL appear to be good candidates to
model such knowledge. In this paper, we focus on vocabularies and ontologies
that rely on the Semantic Web and discard other IoT data models (such as
Bluetooth Low Energy profiles or IPSO Smart Objects). We were able to find
five projects in the literature where an IoT vocabulary was formalized with
RDFS/OWL and used in concrete applications:
– IoT-lite4 used in the European project FIESTA-IoT;
– the Smart Appliance REFerence ontology (SAREF)5 , standardized by ETSI
under the name SmartM2M [1];
– OWL-IoT-S6 , as part of the reference architecture IoT-A [12];
– IoT-O7 with connections to the standard oneM2M [15];
3
https://www.w3.org/WoT/IG/
4
http://purl.oclc.org/NET/UNIS/fiware/iot-lite
5
https://w3id.org/saref
6
http://purl.oclc.org/net/unis/OWL-IoT-S.owl
7
https://www.irit.fr/recherches/MELODI/ontologies/IoT-O
An Ontology for WoT 3
– SA8 from another European project, CHOReOS.
All these vocabularies aim at reusability and modularity, both in the sense
that they rely on upper ontologies but also that they are supposed to link domain
models to applications. As such, they are sometimes refered to as horizontal
vocabularies.
Our selection is included in the list of vocabularies the authors of IoT-O
surveyed [16]. However, since horizontality was our main selection criterion, we
discarded some of the vocabularies they considered that we believe to be either
application models (such as oneM2M and SPITFIRE ontologies), or domain
models (SSN, to some extent).
Each of these vocabularies imports other vocabularies, which in turn also
import external vocabulary modules. As a result, all the vocabularies directly
or indirectly involved in the specification of concepts of the IoT form a directed
graph, part of the so-called Linked Open Vocabulary (LOV) cloud9 . To facilitate
our analysis of IoT vocabularies, we constructed the subgraph with the five vo-
cabularies mentioned above as sources. We followed the LOV methodology [18],
that defines different types of alignment between vocabularies: import as pre-
viously mentioned but also extension, specialization, generalization, equivalence
and disjunction10 . Such relations can be materialized by SPARQL construct
queries. The result is shown in Figure 1 (top).
The resulting graph includes 51 vocabularies (vertices) and 120 alignments
between them (edges). An interesting characteristic to show is the level of reuse
of a given vocabularies (i.e. the in-degree of the graph vertices). Thus, in the
figure, the size of the vertices is proportional to their in-degree, which brings
two vocabularies out: Dolce+DnS Ultralite (DUL)11 and, to a lesser extent,
SSN12 . SSN itself specializes DUL. A couple of other graph metrics can also be of
interest: in average, a vocabulary is aligned with 5.0 other vocabularies (average
vertice degree); the average indirect alignments with a given vocabulary is 3.7
(average path length); the maximum indirect alignments for a vocabulary equals
8 (graph diameter). At last, it is worth noting that the sizes of the vocabularies
(i.e. the number of axioms they define) are extremely unbalanced, as shown on
the figure. DogOnt, SWEET and QUDT account for most of the vocabulary set
from that point of view.
This analysis of the LOV segment for the IoT could allow us to quantify the
reusability and modularity of the above mentioned source vocabularies. However,
our goal is less to compare them than to identify the parts they have in common.
It turns out that all IoT vocabularies eventually align with DUL, partly through
SSN. SAREF is the furthest vertice from DUL (shortest path of length 3).
8
http://sensormeasurement.appspot.com/ont/sensor/hachem onto.owl
9
http://lov.okfn.org/
10
No subject vocabulary generalizes some object vocabulary without having the object
vocabulary specializing the subject vocabulary. We therefore discard generalization
in the figure.
11
http://www.ontologydesignpatterns.org/ont/dul/DUL.owl
12
http://purl.oclc.org/NET/ssnx/ssn
4 Victor Charpenay et al.
Fig. 1: IoT fragment of the LOV cloud (top) with hypothetical alignments be-
tween vocabularies for physical quantities (middle) and Web services (bottom).
Source vocabularies are marked in red, and our contribution (including imports)
in blue. The names of vocabularies not further mentioned in the paper have been
omitted
An Ontology for WoT 5
On the other hand, the graph diameter is relatively high. 3 or 4 would have
been a more adequate value. The redundancy of definitions in the domain of
physics is in part responsible for spreading the graph. There are as many domain
vocabularies for quantity kinds and units as source IoT vocabularies. QUDT,
SWEET, UCUM are dedicated vocabularies that rely on standardized units and
quantity kinds, while DogOnt and SAREF redefine them according to their own
need. Figure 1 also shows what the IoT LOV fragment would be if all these
vocabularies were aligned, with SWEET as reference (middle). Similarly, we
observed redundant conceptualizations for Web services (either through OWL-S
or WSMO). Finally, Figure 1 (bottom) represents the graph as envisioned by
WoT, where vocabularies for Web services align with our ontology. More details
towards possible alignments are presented in Section 4.
2.2 Ontologies for the Web
Most of the vocabularies we have reviewed so far include a way to describe
how to serve IoT data, that usually turn out to be Web services with a specific
endpoint URI. Yet, the Web is first a collection of interlinked resources (uniquely
identified by a URI) and resources are the only artifacts agents can interact with.
To be able to use a Web service, an autonomous agent has first to understand
the interplay between the underlying Web resources.
As an illustration, IoT-A relies on OWL-S to semantically describe IoT Web
services while IoT-O favors the Web Service Modeling Ontology (WSMO). It
has been shown in the past that these two concurrent approaches are hardly
interchangeable and unable to capture the semantics of RESTful Web services,
that precisely exploit the centrality of resources on the Web [9]. As RESTful
services play an important role in the Web of Things, it is possible to find a
conceptualization for Web resources in the recently started ASAWoO project
[11], combining definitions from the Hydra vocabulary [10] and schema.org13 .
As a matter of fact, the concepts behind Web resources and URIs have regu-
larly been the subject of discussions since the creation of the World Wide Web.
In the past, refinement in their definition was required to fit the idea of a Seman-
tic Web, where any entity in the world could then be given a URI. This question
of identity on the Web anticipated the idea of Web resources that would allow
to interact with the physical world [14].
The pattern that emerged around (Semantic) Web resources involves three
concepts: Identifier, Resource and Entity (therafter refered to as the IRE onto-
logical pattern). It has been formalized both with predicate logic axioms [6] and
OWL [13]. We develop the principle behind IRE in the next section, immedi-
ately followed by our proposal for a Web of Things ontology, that consists in an
extension to IRE that includes WoT resources.
13
https://schema.org/
6 Victor Charpenay et al.
3 An Ontology for the Web of Things
3.1 Identifier, Resource, Entity
The IRE pattern is essentially based on the idea that Web resources can act
as addressable proxies for real-world entities. They have a unique Web location
associated to a resoure identifier (aka a URI) and involve a given resolution
method to be accessed. Real-world entities do not have a URI themselves.
Several types of proxy relations are materialized in IRE. For instance, a re-
source is either an informal or a formal proxy, if it e.g. relies on OWL. A resource
in DBpedia is formal while its Wikipedia counterpart is informal. Moreover, a
resource is an exact proxy if it relates to a single entity.
In terms of OWL, IRE defines the following concepts: WebResource, Proxy-
Resource and SemanticResource. It imports concepts from DUL, namely En-
tity, InformationEntity and InformationRealization. The following roles
are also defined by IRE: proxyFor, exactProxyFor, formalExactProxyFor and
informalExactProxyFor.
Definition 1 details IRE axioms in Description Logic (DL) notation, conform
to the OWL DL profile. We separated role axioms (RBox) from terminological,
concept axioms (TBox) as per DL theory. An updated version of the original
OWL ontology is available at http://w3c.github.io/wot/w3c-wot-td-ire.owl. Be-
sides namespace updates, we aligned WebResource with the concept Resource
from Hydra, which is rather straightforward.
formalExactProxyFor v proxyFor,
informalExactProxyFor v exactProxyFor v proxyFor
—
WebResource v InformationRealization v InformationEntity,
ProxyResource ≡ WebResource u ∃proxyFor.Entity,
SemanticResource ≡ ProxyResourceu ≤ 1.formalExactProxyFor.Entityu ≥
1.formalExactProxyFor.Entity
Definition 1: RBox & TBox of the IRE ontological pattern
3.2 Thing Description, Interaction
The IRE ontological pattern helped define the Semantic Web, which is now
supported by a large number of recommendations from the W3C. Today, as the
W3C embraces the idea of extending the Web to the physical world, IRE might
again prove relevant in the design of the Web of Things. We further detail WoT
concepts in the following.
The discussions within the W3C WoT interest group —which the authors
took part in— first focused on defining semantic models for “Things” and their
An Ontology for WoT 7
capabilities. On the basis of these discussions, it appeared that only a few con-
cepts were needed for WoT agents to start to communicate with each other.
Alongside the implementation work done within the interest group, two main
concepts have emerged. First, WoT Things should describe themselves and be
able to exchange their description with other agents. We call such a description
a Thing Description (TD). Second, WoT Things should expose to the Web a set
of Interactions, which corresponds to their interface to the physical world. More
details can be found on the working documents of the interest group14 .
TDs are the main vector for interoperability. It is clear that a semantic de-
scription of the real-world entities a Thing interacts with should be included in
its TD. Yet, it has not been clear so far what kind of Web resources Interactions
are and how they relate to real-world entities. We propose a textual definition of
both TDs and Interactions, from which an OWL conceptualization naturally fol-
lows, building on IRE. Our OWL conceptualization should help map real-world
entities to Web resources and eventually materialize such mappings by means
of alignment between IoT vocabularies and the meta-model of WoT. We define
TD and Interaction as follows:
Thing Description Semantic resource formally describing a unique WoT Thing
that a software agent can interact with. Examples of WoT Things include
building rooms, manufactured products, mechanical systems but also digital
control devices, i.e. any real-world entity without a priori restriction.
Interaction Web resource of arbitrary content format acting as a digital proxy
for any real-world entity that is not already digital information. Such entities
can be physical quantities like temperature or pressure, natural phenomena
like raise of temperature or object motion, arbitrary states like on/off, etc.
From the above definitions, we can state that ThingDescription subsumes
SemanticResource and Interaction subsumes ProxyResource.
Even though we do not formulate a priori constraints on the entities related
to Interactions, the implementation work of the interest group revealed recurrent
interaction patterns that could further specialize our definition. These patterns
are usually referred to as the concepts Property, Action and Event. We included
these concepts in the ontology as proxifiable entities but left their definition
empty, as different interpretations still coexist within the group.
The complete axioms are detailed in Definition 2. The OWL ontology is also
available at http://w3c.github.io/wot/w3c-wot-td-ontology.owl.
3.3 An Example
Figure 2 gives a concrete example of TD, as per definition of the W3C inter-
est group. It uses the JSON-LD format, a JSON serialization format for Linked
Data. No prior knowledge of our ontology is needed to design a TD. In fact, we
have got interested in semantically characterizing it while implementations of
14
http://w3c.github.io/wot/current-practices/wot-practices.html
8 Victor Charpenay et al.
isThingDescriptionOf v formalExactProxyFor,
isInteractionOf v informalExactProxyFor,
hasThingDescription := isThingDescriptionOf− ,
hasInteraction := isInteractionOf−
—
Thing v Entity,
ThingDescription ≡ SemanticResourceu ≤ 1.formalExactProxyForThingu ≥
1.formalExactProxyForThing,
Interaction ≡ ProxyResourceu ≤ 1.informalExactProxyFor(Entity u
¬InformationEntity)u ≥ 1.informalExactProxyFor(Entityu¬InformationEntity),
Property t Action t Event v Entity,
∃forProperty.> v Action t Event,
> v ∀forProperty.Property
Definition 2: RBox & TBox of our ontology for WoT
WoT agents capable of generating and processing TD documents were already
available. The only element needed is the context URI provided by the W3C,
https://w3c.github.io/wot/w3c-wot-td-context.jsonld, where are defined map-
pings between keywords like uris, hrefs or properties and Semantic Web en-
tities (here, hasThingDescription, hasInteraction and hasProperty, respec-
tively). Based on these mappings, RDF serialization of this JSON document is
possible. In addition, a standard-compliant OWL DL reasoner can infer implicit
knowledge from the RDF data and our WoT ontology. The RDF representation
of the example also presented in Figure 2 includes inferred data.
This example describes a Thing named “TD example”. It is formally de-
scribed by two TDs (one of them is the JSON sample itself) and it has relations
to at least two entities. The first of these entities is a Property, which is proxyfied
by the Interaction with the URI coap://thing.example.org/val and the second
one is an Action, proxified by another Interaction that acts on the Property
(property maps to onProperty).
Several aspects are of importance in this example. First, all real-world entities
are blank nodes (i.e. they have no URI). Indeed, if they had a URI, they would
then either be Interactions or semantic resources. They can have specific local
identifiers, though (e.g. "@id": " :val").
Moreover, there is no one-to-one mapping between real-world entities and
resources, which explains why uris and hrefs are arrays and can contain more
than one URI. For instance, if the same Property can be accessed both with
HTTP and CoAP, it will have two Interactions. Similarly, the same data can be
represented in different formats like XML or EXI, potentially by two distinct
Web resources, and still realize the same entity.
At last, anything can potentially be an Interaction as long as it has a URI.
The IoT relies on a wide range of protocols, including BLE and MQTT, which do
not officially support URIs. According to IRE and our WoT ontology, adopting
An Ontology for WoT 9
{
"@context": [
"http://...w3c-wot-td-context.jsonld",
{"dogont": "http://.../dogont.owl#"}
],
"name": "TD example",
"@type": "dogont:Lamp",
"uris": ["coap://thing.example.org/"],
"properties": [
{
"@id": "_:val",
"@type": "dogont:ColorStateRGB",
"hrefs": ["val"],
"valueType": "xsd:integer",
"writable": true
}
],
"actions": [
{
"@type": "dogont:ToggleFunctionality",
"hrefs": ["tg"],
"property": "_:val",
"outputData": {
"@type": "dogont:OnOffState",
"valueType": "xsd:float"
},
"inputData": {}
}
],
"events": []
}
Fig. 2: Thing Description example in JSON-LD (left) and RDF (right)
a scheme officially endorsed by IANA is the only criterion for these protocols to
be part of the Web of Things.
One can note as well that the TD is annotated with external types from
DogOnt, that belong to the IoT LOV segment. Without these annotations, it
would indeed be hard if not impossible to understand that the Thing we take as
example is actually a LED lamp. We deal with this subject more in details in
the next section.
4 Relationship to Other Ontologies
As pointed out by the TD example, semantic interoperability in WoT stems from
annotating a Thing Description with external vocabularies. In our proposal, we
defined the concepts Thing, Property, Action and Event but we intentionally
left open some aspects of their definition. They are thought of as placeholders for
alignments with existing IoT vocabularies. We have investigated different means
towards alignment based on our ontology, which we present in the following.
Yet, we assume that concrete alignment with WoT (i.e. the model the W3C will
adopt eventually) should be carried out by the authors of the vocabularies we
have reviewed in Section 2.1.
When TD elements declare types like Lamp, ColorStateRGB or ToggleFunc-
tionality, implicit alignment between DogOnt and our WoT ontology is meant
(e.g. through a specialization relation). This alignment at instantiation time
might be assisted by automatic reasoning: confronting axioms from the imported
ontology with those from our ontology, a computer program could check whether
the TD is satisfiable or not. In practice, for such program to be relevant, the two
vocabularies should be aligned with a common upper ontology. As highlighted by
10 Victor Charpenay et al.
our analysis of IoT vocabularies, DUL appears to play that role. Here is an ex-
ample: if, instead of "@type": "dogont:ToggleFunctionality", the lamp TD
had declared "@type": "dogont:ToggleCommand" for the Action, it would have
led to unsatisfiability. Indeed, Action subsumes ¬InformationEntity while
ToggleCommand is declared in IoT-O as a subclass of InformationEntity, mean-
ing they are mutually disjoint.
We implemented a validation tool that follows this principle, available at:
https://github.com/thingweb/thingweb-playground. Ontologies usually have var-
ious expressiveness levels, mostly RDFS, OWL Lite or OWL DL. We used the
HermiT reasoner15 , that is capable of reasoning over OWL DL axioms. Such
reasoners are known to have scalability issues but since the vocabularies we con-
sider often have reasonable sizes, our tool is able to solve the satisfiability task
within a few seconds. We hope this tool will also help evaluate our conceptual-
ization of WoT by members of the W3C interest group: the higher the number of
satisfiable TDs with annotations from the IoT LOV fragment, the more accurate
our model.
Furthermore, besides exploiting logical axioms, one can also see vocabulary
definitions as graphs and explore the connections between them at a syntactic
level. Inspired by well-known structure-based ontology matching techniques [5],
we searched for possible matches with the IRE pattern in the IoT LOV fragment.
More precisely, we first identified matches for Entity and Identifier and then com-
puted the shortest path between them. To match concepts with Entity, we used
our definition of WoT proxifiable Entity, that is, Entityu¬InformationEntity.
On the other hand, matches for Identifier are usually not directly materialized
in vocabularies. Instead, we looked for data type restrictions on the RDF data
type xsd:anyURI. We found 26 data type properties from 7 distinct vocabularies
and used them as sources. The algorithm runs until the node Entity (DUL) is
found. The paths we found are shown in Table 1 (excluding loops).
Property path Defined by
^hasOutputType/hasAccessInterface/hasServiceEndpoint OWL-IoT-S
isAssociatedWith/endpoint IoT-lite
hasService/hasOperation/hasAddress WSMO-lite
Table 1: Possible paths between Entity and Identifier from the IoT LOV
fragment (SPARQL property path syntax)
Hypothetical alignments between these vocabularies and WoT based on the
matches we found do not significantly change the graph metrics of the IoT LOV
fragment (average vertice degree, average path length, diameter). Yet, interest-
ingly, all paths starting from Identifier eventually lead to the concept of Ser-
15
http://www.hermit-reasoner.com/
An Ontology for WoT 11
vice (with a maximum path length of 4). Four vocabularies have a definition
for Service: SAREF, OWL-S, WSMO-lite and IoT-lite. As already mentioned,
some of them are not compatible but they have that in common that they all
rely on Identifiers, hence on the Web architecture. Since Resource is the atomic
concept of the Web architecture, all modelings of services are theoretically com-
patible with our WoT model, what speaks in favor of using WoT as a basis for
ontological alignment.
5 Conclusion
Our work concentrated on defining semantics for the Web of Things, based on
the activity of the W3C. The recommendations that the group will publish will
prove us right or wrong, with respect to the definitions we give for TDs and
Interactions in this paper. However, in the mean time, we hope that our WoT
model will be used as a framework by W3C members, as illustrated with our
validation tool, and assist further development of the semantic models involved
in WoT.
Another aspect of our work was the materialization of the LOV fragment
for IoT. It revealed, among others, the dominant position of DUL. We there-
fore turned our work towards DUL as well, and chose a vocabulary (IRE) that
provided alignment with DUL. This characteristic facilitates alignment of WoT
with existing IoT vocabularies. Yet, the IoT LOV fragment is still broad and the
need for light-weight semantics involving a limited set of concepts —some kind of
“iot.schema.org”— has been clearly expressed by the industry. We believe that
one way to achieve this is by progressively tightening the IoT LOV fragment by
means of alignments. As it is expected that the number of resources on the Web
that relate to real-world entities will rapidly increase, an early conceptualization
of WoT resources was needed to achieve that goal.
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