=Paper=
{{Paper
|id=Vol-2213/paper7
|storemode=property
|title=SmartEnv Ontology in E-care@home
|pdfUrl=https://ceur-ws.org/Vol-2213/paper7.pdf
|volume=Vol-2213
|authors=Marjan Alirezaie,Karl Hammar,Eva Blomqvist,Mikael Nystrom,Valentina Ivanova
|dblpUrl=https://dblp.org/rec/conf/semweb/AlirezaieHBNI18
}}
==SmartEnv Ontology in E-care@home==
https://ceur-ws.org/Vol-2213/paper7.pdf
SmartEnv Ontology in E-care@home
(ShortPaper)
Marjan Alirezaie1 , Karl Hammar2,3 , Eva Blomqvist2 , Mikael Nyström4,5 , and
Valentina Ivanova2
1
Center for Applied Autonomous Sensor Systems, Örebro University, Sweden
2
RISE SICS East AB, Linköping, Sweden
3
Jönköping University, Sweden
4
Department of Computer and Information Science, Linköping University, Sweden
5
Department of Biomedical Engineering, Linköping University, Sweden
Abstract. In this position paper we briefly introduce SmartEnv ontol-
ogy which relies on SEmantic Sensor Network (SSN) ontology and is used
to represent different aspects of smart and sensorized environments. We
will also talk about E-carehome project aiming at providing an IoT-based
health-care system for elderly people at their homes. Furthermore, we re-
fer to the role of SmartEnv in Ecarehome and how it needs to be further
extended to achieve semantic interoperability as one of the challenges in
development of autonomous health care systems at home.
Keywords: SmartEnv Ontology · E-Health · Semantic Interoperability.
1 Introduction
In the era of the Internet of Things (IoT) and advances in sensor technology,
smart environments and their applications are becoming more ubiquitous. By
smart environments we mainly refer to sensorized environments that provide
domestic monitoring and cognitive assistance services for their inhabitants. The
Semantic Sensor Network (SSN) ontology developed by the W3C Semantic Sen-
sor Networks Incubator Group (SSN-XG) is a generic representation model to
describe sensors, observations and their related concepts [2,3,4,5]. Using such
generalized ontologies facilitates the process of design and development of sensor-
based computational systems such as applications of smart environments.
In this work which is a position paper, we briefly introduce the SmartEnv
ontology which relies on SSN, as a model suggested to represent different as-
pects of smart environments. We then discuss why and how SmartEnv needs
to be extended to achieve semantic interoperability in the health domain. It is
worth mentioning that the SmartEnv ontology has been published as an ontology
description article [12] but has not yet been presented to the community.
After a brief introduction to SmartEnv, we also introduce E-carehome project
as one of the use cases of SmartEnv in the health care domain. A current vision
in the area of ICT-supported independent living of the elderly involves populat-
ing the home with electronic devices i.e. sensors and actuators, and linking them
SmartEnv Ontology in E-care@home 73
to the Internet. In E-carehome creating such an Internet-of-Things (IoT) infras-
tructure is done with the ambition to provide automated information gathering
and processing on top of which e-services can be built. At the end, we suggest
how SmartEnv can be extended to provide semantic interoperability which is
required to bring health care services to patients’ homes.
2 SmartEnv Ontology
In order to support the use of artificial intelligence techniques for automating the
provision of different services in smart environments, it is necessary to describe
the capabilities of the various aspects of such environments. These descriptions
that have been studied in the literature [6,7,8,9] include physical aspects (e.g.,
the structure of the environment, sensor network setting or entities), as well as
conceptual aspects (e.g., events or activities of the users), and can be modeled
in ontologies. The details of the literature study can be found in [12]. During the
requirements analysis process, we considered a number of (conceptual) aspects
of smart environments to be covered in the ontologies:
Observation/Sensing Observing of an object or a place is the main motivation
why the environment is sensorized. A representation model is required to answer
questions such as what can be observed by a certain sensor? To what object is
a sensor attached? What is the location of the object, and what does the sensor
measure? Can the sensor or its holding object move?
Agents Agents (e.g, inhabitants of a home) are the main characters whose
activities, locations, or more specific parameters such as safety and health are
usually the main reason behind any observation process in a smart environment.
A representation model is required to answer questions such as what are the
possible activities of the agent? Can the agent be targeted by sensors? Where is
the agent now? What is the agent doing now?
Activities/Events Any changes in a smart environment are represented in the
form of an event or an activity. Questions such as when an event has occurred,
or why such event was recognized, can be answered by representing activities in
terms of their preconditions.
Objects Physical objects are also directly or indirectly the target of the ob-
servation process in order to recognize activities in a smart environment. We
represent objects to answer questions about their state (being in a specific situ-
ation), locations, or the events or activities in which they are involved.
Network set-up In order to set-up a smart environment a sensor network
deployment related to the observation process, is indispensable. A network rep-
resentation model is used to answer any question regarding the hardware and
software configuration of a network, its components and their locations.
74 M. Alirezaie et. al.
dul:isObservableAt
Time
dul:isObservableAt
Interval TemporalEntity
⊑ owl-time:Interval ⊑ owl-time:TemporalEntity
⊑ dul:TimeInterval Event
Event
⊑ dul:Event
Interval EventCondition
TemporalDistance
⊑ owl-time:Interval
dul:hasPrecondition
dul:hasParticipant Manifestation ComplexEvent
Geometry Object dul:isEventIncludedIn
dul:isSettingFor
se-geometry:SpatialObject se-object:Object
⊑ dul:Object
ssn:hasProperty
sosa:FeatureOfInterest ssn:Property
geop:hasGeometry
hasSpatialRelation
se-object:NodeHolder se:object-SmartObject
geop:Geometry ⊑ sosa:Platform dul:isExpressedBy
ssn:forProperty
Situation
se-object:MobileObject se-object:Agent
dul:hasLocation ⊑ dul:Agent Situation
Place ⊑ dul:Situation
dul:isExpressedBy State
se-place:Section
⊑ dul:PhysicalPlace ⊑ dul:InformationObject
sosa:hosts sosa:observedProperty
sosa:hasFeatureOfInterest
dul:hasPart
Network
Sensing
se-place:SmartEnvironment Network ssn:hasSubSystem
⊑ dul:PhysicalPlace ⊑ ssn:System Observation
⊑ sosa:Platform NodeStation ⊑ sosa:Observation
ssn:hasDeployment ConfigurationProcedure
NetworkModule ⊑ sosa:Procedure
SenderNodeStation sosa:madeObservation
ssn:hasSubSystem
⊑ ssn:System
ssn:inDeployment Sensor ssn:implements
Deployment
⊑ sosa:Sensor
⊑ ssn:Deployment Node ssn:hasSubSystem
Legend
Ontology Pattern
SubSumption
OWL Class Object Property
(based on existential
External Class restriction or cardinality)
Fig. 1. SmartEnv Ontology is composed of 8 ontology patterns representing different
aspects of a smart environment.
Spatial aspect Any physical entity such as objects, agents, and places in a smart
environment has a geometrical aspect based on which their spatial relations with
the environment can be represented.
Temporal aspect Similar to spatial aspects, the temporal aspects are the main
basis of an observation process. A temporal representation model is used to an-
swer questions such as when the occurrence of an activity is realized. It also
allows us to define activities based on the temporal relations with their precon-
ditions.
The aforementioned aspects have been modeled in the form of 8 ontology
patterns shown in Figure 1. In the following subsections, we briefly introduce
each pattern whose representational details can be found in [12].
2.1 Time Pattern
The Time pattern6 represents any temporal entities that we may use to represent
things in a smart environment. In order to represent the temporal aspect of
such environments, this pattern has been designed as an extension of the OWL-
Time ontology, a W3C recommendation for describing temporal concepts [1].
6
https://w3id.org/smartenvironment/patterns/time.owl
SmartEnv Ontology in E-care@home 75
The OWL-Time ontology provides precise representation for temporal entities
in the form of either time instant or temporal duration. In the context of smart
environments, we, however, require more specific temporal representation that
allows us to also represent relative temporal distance (for example, between an
event and its preconditions). For this, we have extended the OWL-Time ontology
and introduce it as our Time ontology pattern. In this pattern, we define three
types of temporal entities representing time instants, time intervals and temporal
distances.
2.2 Geometry Pattern
Apart from the temporal aspect, in a sensorized environment, specifically when
there are mobile agents such as robots, the representational model needs to also
cover the spatial aspects of entities including the topology of objects, rooms,
etc. For this, we have designed a pattern called Geometry7 relying on the
upper level spatial-related ontology GeoSPARQL [10] and the Open Time and
Space Core Vocabularies [1]. The OGC GeoSPARQL standard together with the
Open Time and Space Core Vocabulary Specification (which provides qualitative
directional relations) define an adequate vocabulary for representing geospatial
data enabling qualitative spatial reasoning based on geometrical computations.
2.3 Situation Pattern
A “situation” illustrates a specific state of a “feature of interest” (e.g., the tem-
perature of the living room is warm)8 . By feature of interest we refer the concept
defined in the SSN ontology as an object which is the interest of the observation
process. Although states are usually time dependent, we decided to keep the rep-
resentation of a situation as abstract as possible for the sake of generality. The
concept of situation can be augmented with the concept of time in other patterns
such as event-related patterns which are associated with temporal properties.
2.4 Sensing Pattern
A sensing process is simply defined as the process of monitoring a specific prop-
erty of a feature of interest using a sensing device. In order to represent such
concept, we have designed the pattern Sensing9 which is highly relying on the
SSN ontology allowing us to model establishment of a sensing process.
2.5 Place Pattern
The meaning of a place in the context of smart environments is twofold. First,
by a place we mean the entire smart environment which holds the deployment
7
https://w3id.org/smartenvironment/patterns/geometry.owl
8
https://w3id.org/smartenvironment/patterns/situation.owl
9
https://w3id.org/smartenvironment/patterns/sensing.owl
76 M. Alirezaie et. al.
of a sensor network and might also be composed of several sections. The second
meaning of a place refers to each section of the main place with a specific identity
that can be as such seen as a location for different objects. Given this preliminary
definition, the pattern Place10 defines a place as a specialization of the class
dul:PhysicalPlace.
2.6 Network Pattern
A network in a smart environment is defined as a system containing different
types of devices such as nodes and node stations. By node, we mean a commu-
nication module that indicates either a sending or a receiving data module in a
network. It is worth mentioning that the current design of the Network Pattern
only supports the request/response communication paradigm.
Each node depending on its type can be a part of a node station representing
another type of device that contributes in establishing a network. Each node sta-
tion contains a node along with other things including a sensor, power supplies,
batteries etc.
The whole process of a network set-up regardless of its exact technical de-
tails is represented by a non-physical concept called deployment. The pattern
Network11 unifies the representation of environment automation installations
that can be found in different systems.
2.7 Object Pattern
The pattern Object12 allows us to define objects based on their impor-
tant features or abilities in the context of smart environments. The class
dul:PhysicalObject provides a suitable representational basis for the objects’
taxonomy, which we have categorized into two types of smart objects and node
holders. By smart object we refer to those objects that are the interest of an
observation process (i.e, feature of interest). Due to the usual difficulties of in-
stalling sensors in a smart home, it is common to use some other objects (i.e.
node holders) to host sensors. This separation provided by this pattern is specif-
ically useful for other computational modules such as a configuration planner
one of whose tasks is checking the status/functionality of sensors by sending a
robot to their locations.
Each smart object (or a feature of interest) has at least a property to be
observed. Another categorization of smart objects that has been considered in
the object pattern, is about their mobility. An objects is considered as mobile
only if its location as one of its properties, can change. In order to also be able to
reflect the spatial relations between objects (e.g., the “fridge is connected to the
cupboard”), or between an object and a place (e.g., “the bed is located at the
left side of the bedroom”), it is required to define objects in a smart environment
also as a se-geometry:SpatialObject defined in the pattern Geometry.
10
https://w3id.org/smartenvironment/patterns/place.owl
11
https://w3id.org/smartenvironment/patterns/network.owl
12
https://w3id.org/smartenvironment/patterns/object.owl
SmartEnv Ontology in E-care@home 77
2.8 Event Pattern
The pattern Event13 is an extension of the representation of events in DUL. In
this extension we have defined two different types of events including a manifes-
tation and complex event. By manifestation, we refer to those events that can
be directly captured from sensor data and represent the occurrence of a smart
home situation through a sensing process. However, the latter event type, as its
name indicates, represents more complicated events whose occurrence depends
on several preconditions [11]. Each precondition as such represents a specific
situation assumed to be observed within an interval with a specific temporal
distance to the event’s occurrence time. Furthermore, the pattern Time which
is per se based on the OWL-Time ontology, can provide the required basis to
represent the temporal properties of the smart environment to capture changes
in the form of events or activities.
3 Semantic Interoperability in E-care@home
As an application of the SmartEnv ontology we can refer to health care monitor-
ing and services, where patients are being monitored in their own living environ-
ment. The E-care@home project14 is aiming at providing an IoT-based health-
care system which is composed of various types of sensors continuously moni-
toring both environmental and medical features related to an elderly person[13].
The elderly user of the system is assumed to have specific needs and potential
medical conditions, but is still living at home.
One of the challenges in E-care@home is to achieve semantic interoperability
that allows the monitoring system to combine sensor data with background in-
formation about the patient in order to provide different services for the patient.
These services include recognizing the current situation that the patient is in,
the cause of some events, and the best action for the system to take next. The
background information can be health reports created by the patient or maybe
the patients informal caregivers that are stored in the personal health record
(PHR) system or notes from the home care service, primary health care center
or hospital that are stored in the electronic health record (EHR) systems. For
this to be achieved, the heterogeneity of the services, devices and communica-
tion technologies is a major challenge for expanding generic IoT technologies to
efficient ICT-supported services for elderly. For instance, the ecarehome system
is expected to inform the care giver of the elderly user when it realizes that
his/her heart rate has suddenly increased without any reason such as exercising.
As another feature of interoperability, the system allows to define high heart
rate based on both the health profile of the user (gender, age, health records,
etc) and also the current state of the user (i.e., if he/she is resting or actively
exercising, etc.).
13
https://w3id.org/smartenvironment/patterns/event.owl
14
http://ecareathome.se
78 M. Alirezaie et. al.
4 Extension of SmartEnv
The current version of SmartEnv allows us to represent the context in terms of
environmental settings. To achieve semantic interoperability, SmartEnv needs to
be extended and linked to other ontologies including those that represent health
profile of elderly users (e.g., PHR/EHR15 ) or general medical knowledge e.g.,
SNOMED CT16 .
For instance, the reasoner applied on SmartEnv knowledge needs to also
know about the disease, their causes and their symptoms. Such information has
been already modeled in SNOMED CT ontology, however, not necessarily with
properties compatible with what defined in patterns of SmartEnv. The question
is if we can interlink the SNOMED CT ontology to SmartEnv by redefining a
disease as an event (sub class of the ComplexEvent class in SmartEnv), whose
preconditions are the same as its symptoms (For further information see Event
pattern [12], section 4.8).
Furthermore, many symptoms of diseases are conditional and might change
based on the user’s health profile (i.e., PHR/EHR). Apart from the user’s profile,
the threshold values indicating normal or abnormal state of specific physiologi-
cal parameters such as heart rate or blood pressure also depends on the type of
sensors used in the measurement process. In other words, the representation of
the sensing pattern in the SmartEnv ontology (see sensing pattern [12], section
4.4) which assigns a threshold values for class Sensor might change and be linked
to the ontology representing PHR/EHR information. More specifically, since in
health record of the user, the range of normal and abnormal values (or thresh-
olds) for different physiological parameters are mentioned based on specific types
of sensors, the sensing pattern in the SmartEnv ontology which includes repre-
sentation of sensors, and observation process may also be updated and linked to
other ontologies [14] designed to represent personal health records of the user.
In summary, the next step towards achieving semantic interoperability in
health care includes integration of patterns in SmartEnv with other ontologies
in the health-care domain.
Acknowledgments: The work is supported by the project E-care@home
funded by the Swedish Knowledge Foundation 2015-2019.
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