=Paper=
{{Paper
|id=Vol-2404/paper18
|storemode=property
|title=Towards an Ontology-Based Framework for a Behavior-Oriented Integration of the IoT
|pdfUrl=https://ceur-ws.org/Vol-2404/paper18.pdf
|volume=Vol-2404
|authors=Domenico Cantone,Carmelo Fabio Longo,Marianna Nicolosi-Asmundo,Daniele Francesco Santamaria,Corrado Santoro
|dblpUrl=https://dblp.org/rec/conf/woa/CantoneLASS19
}}
==Towards an Ontology-Based Framework for a Behavior-Oriented Integration of the IoT==
https://ceur-ws.org/Vol-2404/paper18.pdf
Workshop "From Objects to Agents" (WOA 2019)
Towards an Ontology-Based Framework for a
Behavior-Oriented Integration of the IoT
Domenico Cantone, Carmelo Fabio Longo, Marianna Nicolosi-Asmundo,
Daniele Francesco Santamaria, Corrado Santoro
University of Catania
Department of Mathematics and Computer Science
Viale Andrea Doria, 6 - 95125 - Catania, ITALY
{domenico.cantone, fabio.longo}@unict.it, {nicolosi, santamaria, santoro}@dmi.unict.it
Abstract—We present a prototype version of an ontology-based Semantic web is a vision of the web in which machine-
framework, called P ROF -O NTO, that integrates IoT devices and readable data allows software agents to query and manipulate
users with domotic environments. P ROF -O NTO is based on a information on behalf of human agents. In such a vision, web
novel OWL 2 ontology, called OASIS (Ontology for Agents,
Systems, and Integration of Services), modelling behaviors of information carries explicit meaning, so it can be automatically
agents such as IoT devices and users, and other informa- processed and integrated by agents, and data can be accessed
tion concerning user requests, their executions, restrictions and and modified at a global level, thus resulting in increased
authorizations. User requests are performed by automatically coherence and dissemination of information. Moreover, with
selecting compatible devices: agents expose their behaviors and the aid of reasoners, it is possible to infer and process also
are invoked accordingly to what they are able to do on specific
categories of components. OASIS is also used to build semantic implicit information present in the data, thus gaining a deeper
knowledge bases that operate as transparent communication and knowledge of the domain. Automated reasoning systems allow
information exchange systems among agents. one to also verify the consistency of the model and query the
data-set. The definition of a specific domain is widely called
I NTRODUCTION ontology [1], [2].
Automation refers to the capability of devices to act on be- In this paper, we present a prototype version of P ROF -
half of users in specific environments, with a little effort from O NTO,1 an ontological framework for the integration of users
their part. Automation is not confined only to factories, farms, and IoT devices with domotic environments, which acts as a
and cities, but also small environments such as homes can take home assistant. P ROF -O NTO exploits a novel ontology, called
advantage of it. An ecosystem comprising interrelated devices OASIS (Ontology for Agents, Systems, and Integration of Ser-
capable to transfer data over a network and cooperate without vices),2 which models user requests together with restrictions
human control is widely called Internet of Things (IoT). IoT and scheduling, device behaviors, device authorizations imple-
is massively used in smart environments, especially in home mented by smart contracts [3], and information concerning the
automation systems. Domotic systems typically connect web- execution of user requests by devices. With respect to other
enabled devices through internet to a central hub or assistant, Agent System paradigms such as Artifacts&Agents [4], OASIS
which is responsible of managing them. adopts a general approach where agents are entities able to
Currently, many domotic systems are available on the mar- perform actions, whereas components are subjected to actions
ket. Often assistants and devices are strictly bound to their carried out by agents.
providers and thus users are tied to such providers as well. It Transcriptions of user requests are mapped by a BDI rule-
turns out that interchangeability of devices is hardly reachable based system, called PROFETA, in OASIS knowledge bases.
without the intervention of third-part applications and, as a In the current version of the assistant, user requests are
consequence, users may not install devices or assistants de- satisfied by automatically selecting devices whose behaviors
ployed by other sellers. Besides specific marketing strategies, fulfill such requests. Communication between the assistant and
several obstacles prevent the interchangeability of devices. the devices depends on a specific knowledge base of OASIS,
Among them, the most relevant one concerns connectivity, used as a communication protocol, that specifies the device to
networking, and communication protocols whose usage largely be activated, the action to be performed, and the recipient of
depends on the specific IoT applications deployed, which the action.
are to be regarded as black boxes. Moreover, it is almost The paper is structured as follows. Section I provides
impossible to determine a priori what a device is capable to an overview of the semantic web, introducing ontologies
do within the environment and how its functionality can be and their related modelling languages. Section II deals with
controlled by users. Such problems could be solved if devices related works. Section III presents our proposed ontological
were selected on the basis of what they are able to do through
open and shared knowledge bases and if they communicated 1 https://github.com/dfsantamaria/ProfOnto.git
via a common, transparent protocol. 2 http://tiny.cc/OASIS-Ontology
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framework and the way in which it is exploited to extract In [12], a comprehensive ontology for representing IoT
meaningful data. Section IV describes a case-study that shows services is presented, together with a discussion on how it
how our proposed framework can be used in a real application. can be used to support tasks such as service discovery, testing,
Finally, Section V concludes the paper with some hints for and dynamic composition, taking into account also parameters
future work. such as Quality of Services (QoS), Quality of Information
(QoI), and IoT service tests.
I. P RELIMINARIES
A unified semantic knowledge base for IoT, capturing the
Applications that automatically process information, instead complete dynamics of IoT entities and where their heterogene-
of just presenting it, and exchange information with other ity is hidden and semantic searching and querying capabilities
applications need appropriate languages, with formally defined are enabled, is proposed in [13].
syntax and semantics. This issue turns out to be particularly Unification of the state-of-the-art architectures, as put for-
relevant for the web and, in general, for any distributed envi- ward by the scientific community of the Semantic Web of
ronment. The Word Wide Web Consortium (W3C) recommends Things (SWoT), by means of an architecture based on different
the Web Ontology Language (OWL), a family of knowledge abstraction levels, namely Lower, Middle and Upper Node
representation languages relying on Description Logics (DLs) (LMU-N), is described in [14]. The LMU-N architecture
[5], as a solution to this problem and indicates it as a standard provides a reading grid used to classify processes, to which
for representing ontologies. Ontologies are formal descriptions the SWoT community contributes, and to describe how the
of the domain of interest, defined by combining three basic semantic web impacts the IoT.
syntactic categories: entities, expressions, and axioms. In [15], an ontology providing an elementary approach to
OWL, currently in version 2.1, provides users with con- modelling agents’ behaviors and artifacts is proposed together
structs useful for the design of ontologies in real-world with a tool that uses the ontology to generate programming
domains that are not available in the basic semantic web code for agent-oriented software engineering.
model Resource Description Framework (RDF) and in the None of the aforementioned work deals in depth with agent
basic semantic web language RDF Schema (RDFS). As RDF, behaviors or tries to formalize the interaction mechanism
OWL 2 is grounded on the idea of triples or statements, among them. As far as we know, this paper represents the first
each one representing an atomic unit. Triples are ways to attempt of applying semantic web technologies as a commu-
connect two entities or an entity and a data-value, each one nication protocol among IoT devices and as a representation
represented by an Internationalized Resource Identifier (IRI), system for their behaviors and interactions.
i.e., a sequence of characters that unambiguously identifies
a resource within a specific context. Entities represent the III. T HE F RAMEWORK P ROF -O NTO
primitive terms of an ontology and are identified in a unique
way. They are individuals (actors), object- and data-properties In this section, we describe the most important features
(actions), and classes (sets of actors with common features). In of the ontology OASIS, and the software architecture imple-
order to provide a formal description of the domain, OWL 2 menting the prototype version of the ontology-based domotic
triples can be organized into two main categories: expressions assistant called P ROF -O NTO.
and axioms. Expressions are obtained by applying OWL 2
constructs to entities to form complex descriptions, whereas A. The ontology OASIS
axioms describe what is true in the domain.3 OASIS is an OWL 2 ontology consisting of about 120
To retrieve and manipulate semantic knowledge, the W3C classes, more than 100 object-properties, 2 data-properties, and
recommends the SPARQL query language as the standard more than 900 axioms. Among other information, it currently
query protocol for RDF. Like SQL, SPARQL is a declarative models: a) behaviors of agents (i.e., users and devices) in
query language to perform operations on data represented as terms of the operations that they are able to perform; b) agent
a collection of RDF triples. A SPARQL query has a head configurations; c) agent requests; d) executions of operations
and a body: the head comprises a modifier identifying the and their related status.
corresponding type of query, whereas the body consists of an
RDF triple pattern. The reader is referred to [7] for a detailed
overview of SPARQL. Agent
GoalPart TaskPart
II. R ELATED WORK subclass of
subclass of subclass of
dependsOn dependsOn
In the last decade, integration of agent systems and ontolo- hasBehavior
Goal Task
Device Behavior Description Description
gies has been deeply studied in several contexts [8], [9], [10]. consistsOfGoalDescription consistsOfTaskDescription
Concerning IoT, ontological approaches have been focused hasTaskObject hasTaskOperator hasTaskParameter
mainly on sensors, with the purpose of collecting data for
TaskObject Task
TaskOperator
generating perceptions and abstractions of the world [11]. Parameter
3 For a detailed explanation of axioms and expressions introduced in OWL
2, the reader is referred to [6]. Figure 1. Ontology schema of device behaviors
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Goal Task Enviromental
Agent Behavior TaskObject Type
Description Description Component
hasTaskOperator
hasBehavior LightAgent_ hasTaskObject hasType
SwitchOn
LightAgent SwitchOn SwitchOnTask Bulb
Description turn_on
Behavior consistsOfTaskDescription
consistsOfGoalDescription Y
Y
Y
TaskOperator
hasBehavior light_object turn_off
LightAgent_ hasTaskObject _type
SwitchOff
SwitchOff SwitchOffTask
Description
Behavior
consistsOfGoalDescription consistsOfTaskDescription
Class
hasTaskOperator
Instance
Legenda
Figure 2. A representation of a smart bulb device in OASIS
Despite the fact that OASIS is able to represent behaviors consistsOfGoalDescription.
of generic agents, the ontology is mainly focused on devices The core of OASIS revolves around the description of
entrusted by users to perform certain operations within a atomic operations introduced by the instances of the class
domotic context. Behaviors of such agents are described by TaskDescription. Atomic operations are the most simple ac-
the schema in Figure 1. tions that agents are able to perform and that they expose to
The main classes of OASIS and their characteristics for other agents. Hence, atomic operations represent what agents
describing behaviors of agents are summarized next. can do and what they can ask other agents to do by means
- Agent: comprises all the individuals capable of executing of request submissions. Instances of the class TaskDescrip-
actions on the associated components. This class includes, tion are related with three elements that uniquely identify
among others, the classes HumanAgent, SoftwareAgent, the operation. The first element is an instance of the class
and Device, mapping physical people, software or pro- TaskOperator, characterizing the action to perform. Instances
grams, and physical devices, respectively. The class Hu- of TaskDescription are related with instances of TaskOperator
manAgent contains, in its turn, the class User, represent- by means of the object-property hasTaskOperator. The second
ing users that usually access the system. element is an instance of the class TaskObject, representing
- TaskDescription: describes atomic operations (e.g., turn the object recipient of the actions (described by the instances
on, turn off, wipe, and so on) that an agent performs the class TaskOperator) performed by devices. Instances of
as a result of some requests made by other agents. An TaskDescription are related with instances of TaskObject by
atomic operation O may depend on other atomic opera- means of the object-property hasTaskObject. The third element
tions, whose execution is mandatory in order to perform is an instance of the class TaskParameter. It is conceived for
the operation O. Dependencies of atomic operations are complex actions requiring a specific input parameter in order
modelled through the object-property dependsOn, which to accomplish the requested operation, such as the temperature
relates instances of the class TaskDescription. of air conditioners or the light intensity of bulbs. Instances of
- GoalDescription: models a set of atomic operations (rep- TaskDescription are related with instances of TaskParameter
resented by the class TaskDescription), whose execu- by means of the object-property hasTaskParameter.
tions are subject to no order. Execution dependencies of Objects and parameters are related by means of the object-
goals (non-atomic operations) are modelled through the property hasType with instances of the class Type. The latter
object-property dependsOn, relating instances of the class class comprises a set of categories that specialize the type
GoalDescription. Instances of the class GoalDescription of elements introduced in OASIS. For example, floors of
are linked to the related task descriptions by means of buildings are of type “floor” while bulbs are of type “light
the object-property consistsOfTaskDescription. object”.
- Behavior: represents the behavior of a single agent in Figure 2 describes a smart bulb device through its behavior.
terms of what the agent is able to do. It comprises a In particular, the device shows two behaviors, each one con-
set of non-atomic operations (described by instances of sisting of a single goal that, in its turn, consists of a single task.
the class GoalDescription) whose execution is subject to The device is able to perform the actions of turning on and
no order. Instances of the class Behavior are linked to off (i.e., the task operators) the connected bulb (i.e., the task
the related goal descriptions through the object-property object). The latter component is represented by the individual
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bulb. defined by the device in its behavior description by means
Devices are configured in order to fit the needs of some of the object-property setUpFrom.
agents such as users. A device configuration comprises a An example of a component configuration is shown in
set of optional features that users associate with a single Figure 4. The example extends the smart bulb device illustrated
component (recipient of some device operations) or with a in Figure 2 by providing a configuration, introduced by the
single device. There are many features that can be associated user Alan, of the single bulb controlled by a device. Alan
with objects or devices such as virtual collocation, physical specifies the kitchen as physical position of the bulb; in this
position, nickname, and so on. A specific configuration pro- case, the object-property hasSpaceSpan is used to link the
viding information about how devices communicate can be configured object to an instance of the class Space representing
assigned to devices when they are automatically detected by Alan’s kitchen. In this specific smart bulb example, the device
the system. is physically indistinguishable from the controlled bulb. In
Configurations are represented in OASIS according to the such a case, the user configuration should be provided for the
schema in Figure 3, and make use of the following classes: bulb as shown in Figure 4, leaving to the device configuration
- Configuration: models configurations admitted in OA- only the management of the connection information.
SIS. Agents that create the configuration of a device or of a
- ComponentConfiguration: is a subclass of the class Con- component are specified by the object-property configura-
figuration and models configurations associated with the tionProvidedBy, linking an instance of the class Configuration
components of the environment involved as objects in to an instance of the class Agent.
some operations (i.e., instances of the class TaskObject). As stated above, OASIS models user requests consisting in
- DeviceConfiguration: models configurations associated entrusting some devices to do something within the domotic
with devices. environment, in accordance with the restrictions imposed by
- Connection: specifies how to physically communicate their configurations. User requests are introduced by exploiting
with a device, e.g., the protocol used and the commu- classes and properties used to model device behaviors, except
nication address associated with the device. that the desired sets of user goals to be accomplished are
related with a user plan. The ontology schema for user requests
is depicted in Figure 5.
Component setUpFrom Connection
connectsTo
User GoalPart TaskPart
hasConfiguration
hasConnection
requests subclass of subclass of
setsUp Component Device
Configuration dependsOn dependsOn
Configuration subclass of subclass of Configuration
Plan Goal Task
Description consistsOfGoalDescription Description consistsOfTaskDescription Description
configurationProvidedBy
hasConfiguration
hasTaskObject hasTaskOperator hasTaskParameter
subclass of
Agent Device TaskObject Task
TaskOperator
Parameter
Figure 3. Ontology schema of device configurations Figure 5. Ontology schema of user requests
Instances of the class Device are associated with instances User requests are introduced by instances of the class Plan-
of the class DeviceConfiguration by means of the object- Description related to instances of the class GoalDescription.
property hasConfiguration. Instances of DeviceConfiguration The user requesting the action is modelled by instances of
are related with instances of the class Connection by means of the class User that are linked to the plan through the object-
the object-property hasConnection. As stated above, instances property requests. Descriptions of goals from user requests are
of the latter class are exploited for creatiing an entry-point modelled analogously to the descriptions of goals from device
that puts devices in communication. In such a case, the object- behaviors.
property connectsTo is used to link instances of DeviceCon- In Figure 6, we show an example of a user request consisting
figuration with instances of Connection. of turning off a light, performed by the user Alan.
The object-property hasConfiguration is also used to link Finally, a user request is associated with an attempt of
instances of the class Component with instances of the class finding a device that is able to execute it. OASIS uses the class
ComponentConfiguration. Once a component has been con- TaskExecution for representing the attempt of executing an
figured, the instance of ComponentConfiguration is related by agent request. Instances of the class TaskDescription defined
means of the object-property setsUp with a fresh individual by the user requests are related with instances of the class
representing the configured component. The latter individual, TaskExecution through the object-property hasTaskExecution.
in its turn, is related with the non-configured component An instance of Device, representing the device responsible
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Enviromental Component
Task TaskObject Type Space User Type
Component Component Configuration
Description
hasTaskObject hasType hasConfiguration
light_object_ Alan-Light-
SwitchOnTask Bulb User-Alan
type Configuration
configurationProvidedBy
Y
setsUp
setUpFrom hasType
Class Alan-Light Main_Kitchen kitchen
hasTaskObject hasSpaceSpan
SwitchOffTask
Instance
Legenda
Figure 4. Example of configuration in OASIS
of satisfying the request, is related with the instance of In Figure 8, we show an example concerning the execution
TaskExecution by means of the object-property performs. Once of Alan’s request depicted in Figure 6. In the example, Alan’s
the selected device attempts to fulfill a user request, the status request of turning off the light, described in Figure 6, is
of the execution task is updated by linking the instance of accomplished by the smart bulb (represented in Figure 2) that
TaskExecution to an instance of the class ActionStatus. The sets to succeeded the execution status of the action.
most important instances of ActionStatus are the individuals
succeeded status, representing the correct execution of the Task Task
Device
Class Description Execution
action, and the individual failed status, representing the status
of those actions that have not been accomplished. The mod- Instance
elling of the attempt of fulfilling agent requests in OASIS is Legenda
alan-task- hasTaskExecution
1-1-1
summarized in Figure 7.
alan-task- performs
1-1-1-exec Light-Agent
hasTaskObject hasTaskOperator
Plan Goal Task hasStatus
User Description
Description Description
Alan-Light turn_off
succeeded_
status ActionStatus
performs consistsOfTaskDescription
User-Alan alan-plan1 alan-goal-1-1
Task Task
consistsOfGoalDescription
Object Operator
alan-task-1-1-1
hasTaskObject hasTaskOperator
Figure 8. Ontology schema of a task execution
hasType
light-object-type alan-task-object
turn_off
Class
B. Software Architecture
Instance
In this section, we briefly summarize the main features of
Type Task
Object
Task Legenda the architecture of P ROF -O NTO, illustrated in Figure 9.
Operator
Figure 6. Example of modelling of user requests
OASIS KBBehavior
ONTOLOGY PROFETA
hasStatus
Task Task ActionStatus
KBBelief
Device
Execution Description
performs hasTaskExecution
HERMIT DL
REASONER
hasTaskObject hasTaskOperator hasTaskParameter
TaskObject Task
TaskOperator Parameter Figure 9. Architecture of P ROF -O NTO
The core of P ROF -O NTO comprises by OASIS (the ontolo-
Figure 7. Ontology schema of execution of user requests gies illustrated in Figures 1, 3, 5, and 7), the dataset KBBe-
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havior collecting behaviors and user defined configurations of - Device connection and configuration. When a device
devices (RDF graphs of the types depicted in Figures 2 and tries to connect to P ROF -O NTO, PROFETA receives
4), and the data-set KBBelief containing user requests together the request and generates the corresponding knowledge
with their execution information (RDF graphs of the types base of OASIS. This is passed to the ontological core
reported in Figures 6 and 8). of P ROF -O NTO, which in turn updates KBBehavior ac-
P ROF -O NTO knowledge bases are implemented in JAVA by cordingly and executes the HermiT reasoner to check its
exploiting the OWL API [16] and Apache Jena [17], both consistency.
used to manipulate the ontological information and to perform - User requests. When a user requests an action, PROFETA
SPARQL queries. Consistency of P ROF -O NTO knowledge analyzes the command and produces the corresponding
bases is checked by means of the HermiT DL reasoner [18]. RDF graph, which is submitted to the ontological core
P ROF -O NTO also exploits PROFETA [19], a Belief-Desire- of P ROF -O NTO. P ROF -O NTO builds and performs the
Intention (BDI) rule-based system able to trigger rules rep- related SPARQL queries. The result is then sent to
resenting computations to be performed, in order to parse, PROFETA, which activates the selected device. If the
interpret, and manage user requests and device connections. request can be accomplished, the data-set KBBelief is
BDI rules are passed to the P ROF -O NTO core as OASIS updated accordingly. Once the device has performed the
knowledge bases. action, it updates PROFETA by sending the execution
Since most interaction among common users and IoT do- status. Then, PROFETA, in its turn, sends a request to
mains occur through a natural spoken language, P ROF -O NTO the ontological core of P ROF -O NTO, which updates the
manages user utterances via the PROFETA interface. Given data-set KBBelief. Finally, the HermiT reasoner is called
a domotic command either by Speech-To-Text (STT) services by P ROF -O NTO to check the consistency of KBBelief.
or Chatbots, PROFETA processes the string representing the IV. C ASE -S TUDY
transcription of the user intention in natural language by
implementing a robust dependency parser able to deal very We describe a simple case-study illustrating the working
satisfactorily with imperative verbs (without raising the issues basics of the framework P ROF -O NTO. In our example, the
treated in [20]), within a so-called Translation Service (TS). A environment consists of (a) a smart bulb, called light-device,
domotic command usually has the form of an imperative verbal (b) a user, called Alan, and (c) a domotic assistant running
phrase; consequently, the relations generated by the depen- P ROF -O NTO.
dency parser and providing information about user intentions As a first step, light-device is connected to the assistant
have the form dobj(arg1 , arg2 ), namely a direct object, where and suitably configured by the user. The device exposes its
arg1 and arg2 are verb and object related to the intention, behavior by submitting the RDF graph illustrated in Figure
respectively. For example, given the sentence wipe the floor, 10 to the assistant, which integrates it with the P ROF -O NTO
PROFETA produces the relation dobj(wipe, floor). knowledge base as described in Section III. We denote with
prof the prefix of OASIS and with dev the prefix of the smart
Additionally, PROFETA supports other types of relation
bulb ontology.
such as pobj (preposition object), which provides additional
Before fully connecting the smart bulb to the assistant
information about the physical location of an object, and
and integrating it with the domotic environment, the user is
compound/prt (compound/particle), which provides the com-
summoned to configure the device. In the particular case of
positionality of phrasal verbs such as “turn off” or of nouns
the light-device specification, the user may provide a friendly
such as “living room”. The reader can refer to [21] for further
name and indicate the physical collocation of the device. In
details concerning the process of extraction of intentions from
our example, the user Alan sets the name of the device to
natural spoken language.
main kitchen light and collocates it in the main kitchen. Once
The result of the TS module consists of a set of beliefs such information is provided, the configuration manager maps
representing user intentions together with related parameters. Alan’s request into an RDF graph (see Figure 11) and transmits
Such beliefs are processed by the PROFETA engine through it to the assistant.
the use of rules of the form: Subsequently, information provided by the smart bulb be-
havior and the user configuration is merged with the KBBe-
+Int(Verb,Obj,Loc) ≫ generate request(Verb,Obj,Loc), havior knowledge base. Such task is carried out by adding the
triples to the KBBehavior knowledge base and by executing
which produce the desired plan from the set of beliefs obtained the HermiT reasoner for consistency checking.
from previous steps. The plan generate request consists in Once a device is connected to the system, namely once RDF
sending user requests to the ontological core of P ROF -O NTO triples describing its behavior and configuration are integrated
through an appropriate wrapper that maps plans in OASIS in the KBBehavior knowledge base, the system is ready to
knowledge bases of the forms described in Section III-A. For accept user requests, which are formalized as RDF graphs as
the sake of conciseness, details about the syntax of PROFETA well. Request RDF graphs are then merged with the KBBelief
are omitted and can be found in [19]. knowledge base and the HermiT reasoner is executed. In the
P ROF -O NTO takes care of the following two activities: smart bulb example, Alan sends the command “turn off the
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light in the kitchen” to the assistant which generates the RDF The graph returned by the CONSTRUCT query in Figure
graph illustrated in Figure 12. 13 is illustrated in Figure 14.
CONSTRUCT {
dev:LD-turnoff- prof:hasBehavior prof:hasBehavior dev:LD-turnon- ?selected agent prof:performs :alan-task1-1-exec .
dev:light-device
behavior behavior
:alan-task1-1 prof:hasTaskExecution :alan-task1-1-exec .
:alan-task1-1-1-exec prof:hasTaskOperator ?operation .
prof:consistsOfGoalDescription prof:consistsOfGoalDescription :alan-task1-1-1-exec prof:hasTaskObject ?user object .
?user object prof:setUpFrom ?device object . }
dev:LD-turnon-
dev:LD-turnoff- goalDes
goalDes WHERE {
:alan-task1-1-1 prof:hasTaskOperator ?operation .
prof:consistsOfTaskDescription prof:consistsOfTaskDescription :alan-task1-1-1 prof:hasTaskObject ?object .
?object prof:hasSpaceSpan ?space .
dev:LD-turnoff- prof:hasTaskObject dev:LD-turnon-
?object prof:hasType ?obtype .
taskDes taskDes ?space prof:hasType ?spacetype .
prof:hasTaskOperator prof:hasTaskOperator ?config rdf:type prof:ComponentConfiguration .
dev:ld-light1
?config prof:configurationProvidedBy :user .
?config prof:setsUp ?user object .
prof:turn_off prof:turn_on ?user object prof:hasType ?obtype .
?user object prof:hasSpaceSpan ?space user .
?space user prof:hasType ?spacetype .
?user object prof:setUpFrom ?device object .
Figure 10. The RDF graph mapping the smart bulb behavior
?selected agent prof:hasBehavior ?behav .
?behav prof:consistsOfGoalDescription ?goal .
?goal prof:consistsOfTaskDescription ?task .
prof:configurationProvidedBy
?task prof:hasTaskOperator ?operation .
prof:hasConfiguration
base:alan-ld1- ?task prof:hasTaskObject ?agent subject .
base:alan
config
?agent subject prof:hasType ?obtype . }
base:ld1-light1 prof:setsUp
Figure 13. The SPARQL CONSTRUCT query for Alan’s request
base:
base:alan-
main_kitchen prof:kitchen
prof:setUpFrom kitchen
_light
prof:hasSpaceSpan prof:hasType
base:
Figure 11. The RDF graph representing the configuration of the smart bulb base:alan-task1-1-1 dev:ld-light1
alan-task1-1-1-obj
prof:setUpFrom
prof:hasTaskExecution prof:hasTaskObject
base:alan-task1-
base:alan-plan1 base:alan-goal1-1 dev:light-device prof:turn_off
1-1-exec prof:hasTaskOperator
prof:performs
prof:consistsOfGoalDescription
prof:consistsOfTaskDescription
Figure 14. The RDF graph obtained by executing the query of Figure 13
prof:turn_off base:alan-task1-1-1 prof:kitchen
hasTaskOperator
prof:hasTaskObject prof:hasType By means of the graph in Figure 14, the smart bulb can
execute the requested action, thus turning off the light. In fact,
base:
base:
prof:light_object_type
prof:hasType
alan-task1-1-1-obj
alan-task1-1-1-
kitchen
the agent committed to execute the action (dev:light-device),
prof:hasSpaceSpan the operation (prof:turn off ), and the object of the action
(dev:ld-light1) have been correctly spotted and the device can
Figure 12. The RDF graph of the request of turning off the kitchen light be activated by sending to it the requested information.
Subsequently, the device updates the status of the execution
The RDF graph representing Alan’s request is analyzed by task by sending to the assistant the triple:
P ROF -O NTO in order to produce a SPARQL CONSTRUCT
:alan-task-1-1-1-exec prof:hasStatus prof:succeeded status .
query to be executed over the knowledge base KBBehavior and
over Alan’s request graph. The CONSTRUCT query produces if the task has been successfully accomplished, or by sending
an RDF graph that associates Alan’s request with a specific the triple
device able to fulfill his request. The SPARQL query obtained :alan-task-1-1-1-exec prof:hasStatus prof:failed status .
from Alan’s command, consisting in turning off the kitchen if the task has not been performed for some reason. The
light, is illustrated in Figure 13. The body of the query consists assistant updates its belief knowledge base by adding the
of three parts. The first one is taken from the specifications of information relative to the execution status, as provided by
Alan’s request. The second part, obtained from the ontology the device. Afterwards, the assistant is ready to accept new
reported in Figure 3, explores all the user configurations in requests, configurations, or to connect new devices.
order to discover the type of the device and the place the
device has been installed in. The last part, constructed from V. C ONCLUSIONS AND F UTURE W ORK
the ontology in Figure 1, selects an available device fulfilling In this paper we presented P ROF -O NTO, a prototype frame-
the conditions specified in the first two parts. work that integrates users and IoT devices within domotic
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Workshop "From Objects to Agents" (WOA 2019)
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