A Semantic Web Model for Ad Hoc Context-aware Virtual Communities
Application to the Smart Place Scenario
Pierre Maret, Fr
´
ed
´
erique Laforest and Dimitri Lanquetin
Universit
´
e Jean Monnet, F-42000 Saint-Etienne, France
Keywords:
Communities, Mobility, Services, User, Semantic Web, RDF, Ontology.
Abstract:
In this paper, we propose a model for an open framework that allows mobile users to create and to participate
to context-aware virtual communities. The model we propose and implement is a generic data model fully
compliant with the semantic web data model RDF. This model is suited to let mobile end-users use, create and
customize virtual communities. We combine fundamentals for a decentralized semantic web social network
with context-aware virtual communities and services. Smart cities scenarios are typically targeted with this
approach. It can be implemented in places like metro stations, museums, squares, cinemas, etc. to provide ad
hoc context-aware information services to mobile users.
1 INTRODUCTION
A virtual community (VC) is a computer sup-
ported space where information can be shared in-
between stakeholders with common interest or pur-
pose (Preece, 2001; Koch et al., 2002). Millions
of VCs exist on the Internet and they form what is
called the Social Web, where people can join, leave
and participate in many VCs (Bouras et al., 2005). In
this paper, we address the topic of context-aware vir-
tual communities and more specifically we present a
data model and its implementation suited to let mobile
end-users use but also create and customize VCs.
Some community platforms take advantage of the
mobile context of their users (GPS coordinates, IDs
of close devices) to provide a specific service. Ex-
amples are numerous: Personal Smart Spaces (Gal-
lacher et al., 2012), Group Me (Guo et al., 2012),
Meeting Assistant (Zenker et al., 2012; Al Ridhawi
et al., 2012). They use context information to provide
guidance, profile matchmaking or recommendations.
Each platform provides a specific service. Users will-
ing to use (not even to combine) various services face
the so called ”silo” problem and they have to reg-
ister to each platform (Yeung et al., 2009). Also,
these platforms don’t let users define the services they
need in their communities. The platforms propos-
ing user-defined communities require programming
skills. Synthesizing all these remarks, nowadays com-
munity platforms are closed platforms that do not al-
low to take profit to the open world of open data, com-
munities are hardly tunable by their creators without
computing skills, the sharing of profile information or
knowledge between communities and with the open
world is hard or even impossible.
In smart cities, users are supposed to move and use
various services. So, there is need for a model and an
open architecture for users to create the communities
they want, with the services they want, and at the loca-
tion they want (or more generally in the context they
want the services to be used).
In this paper, we propose a model and a platform
architecture for ad hoc context-aware communities:
communities can be created by naive end-users with
a selection of pre-defined semantically described ser-
vices. Users get access to these communities with
a single sign-in. Our model makes use of Seman-
tic Web technologies which allows it to provide self-
describing services and to offer a open door to the
open linked data inand out of the virtual communi-
ties. The semantic web approach promotes open data
model, machine readable data and reasoning, which
let the users be supported by personal software agents
to manage their personal data and social relations in
respect with the chosen policy. Our platforms also
uses the multi-agent paradigm to support users’ in-
teractions. These interactions take advantage of con-
text information to help services tune their offered
functionalities. The multi-agent paradigm empha-
sizes autonomy and cooperation of communicating
entities with goals. Our design typically addresses
smart cities scenarios. It can be applied in places
591
Maret P., Laforest F. and Lanquetin D..
A Semantic Web Model for Ad Hoc Context-aware Virtual Communities - Application to the Smart Place Scenario.
DOI: 10.5220/0004876905910598
In Proceedings of the 16th International Conference on Enterprise Information Systems (ICEIS-2014), pages 591-598
ISBN: 978-989-758-028-4
Copyright
c
2014 SCITEPRESS (Science and Technology Publications, Lda.)
like metro stations, museums, public squares, cine-
mas, park places, etc.
We first present a state of the art related to the
main features for communities: contents, user profile
and services. Then we make a tour of existing com-
munity platforms. In section 3 we present our concep-
tual framework along the three main features from the
state of the art. In section 4 we provide a description
of our implementation, and conclude in section 5.
2 CONTENTS, USER PROFILE
AND SERVICES IN VCS
2.1 Contents
Contents are information pieces exchanged between
users within a community. The underlying data model
impacts the sharing of these contents within the com-
munity platform and out of the platform: inputs,
queries, exports, imports of information can be en-
visioned differently depending on this model. The
model can be either platform dependent or open.
Most social networking websites use their own
data models, specifically designed for their architec-
ture. Moreover, the data model is rarely publicly
available. Platform-dependent data formats make it
nearly impossible to import/export contents, profiles
and social relations (Razmerita and Firantas, 2009). It
leads to the well-known isolation of social networks.
Platform independent data models are public.
Since community platforms run on the Internet and
in a Web environment, norms or standards from these
fields are the best candidates to share models. SIOC
(Breslin et al., 2005; Bojars et al., 2008) has been
proposed for the representation of social data. It is
founded on the Semantic Web technologies. The Se-
mantic Web approach (Berners-Lee et al., 2001) aims
at representing data in terms of RDF
1
triples: (term
relation value) or (term1 relation term2). It is a user-
and machine-readable model. Each item of a triplet
can be described anywhere on the Internet (URI, Uni-
form Resource Identifier). This builds the so-called
linked data. Sesame (Broekstra et al., 2002) and Jena
are Java frameworks used to store RDF triples. Rea-
soning processes can be launched to infer facts, es-
pecially in conjunction with formal description of do-
mains (ontologies) which are also written with RDF
(more specifically using RDF-based languages: OWL
and RDFS). SIOC is written in the RDF/OWL lan-
guage. Its use provides models and techniques for a
platform independent data model.
1
http://www.w3.org/RDF/.
2.2 User Profile
Some platforms (Gallacher et al., 2012; Ganti et al.,
2012; Guo et al., 2012) propose to enrich dynamically
the user profile with context information: physiologi-
cal parameters, GPS coordinates, nearby devices, etc.
The management of several profiles is a complex task
for end-users. Some works have proposed to federate
different profiles (Mitchell-Wong et al., 2007). How-
ever, all characteristics cannot be federated and, for
average users, keeping profiles safe and up to date
quickly becomes an issue. This is crucial because
user profiles contain personal information. A com-
plex management of profiles raises privacy issues:
users may not adequately control data sharing.
A unique profile used on several community plat-
forms eases its management and allows the user to
better control the access rights to personal data. The
profile can be stored either on a remote system or on
the user’s side. Several proposals can be found in the
literature (Seong et al., 2010; Koch and Worndl, 2001;
Grzonkowski et al., 2009). The most advanced model
for profile description is FOAF. It is described as an
ontology, also written in the RDF/OWL language.
2.3 Services in VCs
A virtual community offers services to its participants
to communicate i.e. create, modify, read, share infor-
mation. It can take the form of forum, blog, chat,
private messaging, walls... Services are key compo-
nents of communities as they can strongly influence
participation and adoption. Roles are used to define
different rights of users: a forum can have a moder-
ator, members, experts, etc. Other types of services
can be found such as services related to the location,
to information retrieval, to recommendation, etc.
The services offered on a platform are determined
by the developers or the administrators. Also services
are by definition not available out of the platform and
they are not exportable: they are made for a given
custom architecture and data model. Only some VC
platforms allow users to create customized communi-
ties, but they require programming skills (see 2.4).
When the number of services increases, users
should receive recommendations to select the most
relevant ones regarding their preferences, social re-
lations, and context (Schubert and Koch, 2003). In
(Broens et al., 2004; Meyffret et al., 2010), ontolo-
gies for the descriptions of services, for the user’s
preferences and for context information are used to
improve service discovery and selection. In (Sinner
et al., 2004), a framework is used to match semantic
user profiles with semantic descriptions of location-
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592
based services for recommendation.
2.4 Contents, User Profile and Services
in Existing VCs Systems
Popular social networking websites such as Facebook
use proprietary models. User profiles are stored on
the proprietary platform which is based on a cen-
tralized architecture. Different approaches have been
proposed
2
. Diaspora is a software environment for
distributing the components of the social network at
the users’ sides. Evri has platform-dependent pro-
files and services, however it uses ontologies for the
semantic annotation of contents. dgFOAF (Schwa-
gereit et al., 2010) proposes users to be registered in
a unique community and to launch services from dif-
ferent platforms. Elgg and Liferay allow the develop-
ment of community platforms. Services can be added
as plug-ins (or portlets), making community creation
and upgrading customizable. However, they require
programming skills.
The multi-agent approach has been also used for
VCs (Gunasekera et al., 2012; Aguero et al., 2012).
Koch (Koch and Lacher, 2000) introduces agencies
to separate user profile and community participation
(i.e. use of community services). Guidelines are made
available for people with programming skills wanting
to develop a community platform, with no restriction
on contents or services representation. (Fahad et al.,
2012) is a community platform based on the multi-
agent framework Cartago
3
. End-users can create and
join communities, and they can delegate these tasks to
autonomous agents. The communities are limited to
four services: mailbox, forum, information dispatcher
and personal box.
Properties of community platforms are summa-
rized in table 1. None of these let end-users create
and customize their VCs in selecting services and as-
sociating them to context information. Few platforms
use open (semantic web) models.
3 A DATA MODEL FOR AD-HOC
CONTEXT-AWARE VIRTUAL
COMMUNITIES
In this section, we propose our data model to develop
the framework for ad hoc context-aware communi-
ties. The model relies on RDF, and consists of three
main elements: (i) A user profile based on the FOAF
2
http://diasporaproject.org/, http://www.evri.com/,
http://elgg.org/, http://liferay.com/.
3
http://cartago.sourceforge.net.
model and encompassing also user’s context descrip-
tion, (ii) A community model that extends SIOC with
communities-related properties, and (iii) A service de-
scription model that allows the use of on-the-shelf ser-
vices.
Table 1: Properties of community platforms. (1)has an open
semantic data model; (2)allows multi-platform; (3) imple-
mentation of services.
Platforms (1) (2) (3)
Facebook, Twitter no no developer’s choice
Diaspora no yes developer’s choice
Evri yes no developer’s choice
dgFOAF yes yes developer’s choice
Elgg, Liferay no no developers’ plug-ins
Fahad et al. no yes predefined services
Koch et al. no yes developer’s choice
3.1 User Profile
As we have seen in section 2.2, a unique profile stored
at user’s side is desirable. We use and extend the
FOAF ontology. The model (Fig. 1(a)) has the fol-
lowing properties: foaf:interest (a topic about which
the actor wants to share, obtain, create information),
foaf:friend (another actor known by the user), hasA-
vatar (a delegate of the actor in the systemhosted by
an electronic device of the user), hasGoal (goal to be
achieved by the avatar, see 3.4). Properties foaf:friend
and foaf:interest are taken from the FOAF ontology.
The Actor class refers to both the foaf:Person and the
UserAccount class in SIOC. The SIOC:Avatar class
has no relationship with our Avatar class.
Figure 1: Actor model (a), Avatar model (b).
In addition, avatars have the property hasContext,
which is the capability of the device hosting the avatar
to deliver context data (Fig. 1(b)). We define context
data as a simple set of pairs (descriptor, value). These
pairs are used to represent current information sensed,
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Figure 3: Content (a) and Community (b) models.
Figure 2: Context model.
calculated or known by the device hosting an avatar.
Context data may relate to location but also to battery
state, network connections, user’s behavior (through
an accelerometer), etc. The property hasContextData
is the piece of information describing the state of the
device hosting the avatar or a status of the actor (Fig.
2). It contains hasDescriptor (a string describing the
information) and hasValue (context data value).
3.2 Content and Communities
Contents consist of pieces of information coded in
RDF format with the properties hasAuthor, hasTopic,
type and value which associates a value of a RDF
class defined as a URI (Fig. 3(a)). The sharing of con-
tents happens in the frame of communities. We define
the Community ontology as an extension of the SIOC
ontology (Fig. 3(b)). It is composed of hasOwner,
hasTopic (contents used as subject of the community),
hasMember (actors), hasService (see 3.3), hasRole
(see 3.4) and hasContent (contents shared within the
community). SIOC also provides the notion of topic
Figure 4: Service model.
with a dedicated property that usually refer to a se-
mantic web resource.
3.3 Services
We define a service as a set of operations. For in-
stance, a blackboard is a service, with the operations
write, read and delete. Services in our framework are
related to the management of contents. They are im-
plemented to create and get contents from communi-
ties (hasContent property). As all communities are
described using the data model defined above, any
service can be used in communities. Services are de-
scribed using the ontology given in Fig. 4. It contains
the name of the service, its description and operations.
The Service class of SIOC has been adapted to define
our Service class.
Properties of an operation are described in Fig.
5(a). Notice that the hasRequiredContext property in-
troduces the context of the avatars (see 3.1). This is
used to introduce the context-aware property of op-
erations. The property hasCommand relates to the
exchanges of information: Get, Put, Delete or Post;
it uses hasInputParameter and hasOutputParameter
A parameter (Fig. 5(b)) has a description, a type
(RDF class) and constraints applied on its value (RDF
triple). The hasMultiProperty is used to indicate mul-
tivalued properties.
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Figure 5: Operation model (a) and Operation parameter model (b).
3.4 Goals and Roles
Goals are defined by actors. They are part of their per-
sonal data and consist of sets of operations that avatars
will execute (Fig. 6(a)). The goal model uses part of
the operation’s model defined above to ease service
discovery and selection. Roles define the rights to ex-
ecute operations in the frame of a community. A role
is composed of a set of actors and a set of operations
(Fig. 6(b)). The notion of role also exists in the SIOC
ontology with the class Role that can be referenced by
a UserAccount.
Figure 6: Goal model (a) and Role model (b).
4 FRAMEWORK
IMPLEMENTATION
In the previous section, we have presented the data
model of our framework. It allows users, commu-
nities and services modeling, and more specifically
context-aware operations within communities. It is
based on RDF principles and can be easily extended
or adjusted. In this section, we present the imple-
mentation of this model and we show how it allows
end-users to easily create their customized communi-
ties with different services and for dedicated contexts.
The compliance with the RDF principles is kept with
the framework implementation.
4.1 Main Implementation Choices
Our implementation combines the semantic web tech-
nologies with the multi-agent approach. Indeed,
agents (users, avatars) as well as information and ser-
vices are distributed in different concrete or virtual
(i.e. computer supported) locations. Furthermore
agents interact with others and share information in
the frame of the communities to achieve user’s goals.
We use the multi-agent platform Cartago to im-
plement our system. In Cartago, agents interact with
other agents but also with the environment through
the artifact abstraction. We implement data store,
avatars, communities and services with this abstrac-
tion. Through artifacts, agents can write/read data,
get context values, create and participate to communi-
ties and execute operations. Cartago proposes also the
concept of workspace to group artifacts and agents.
We use this concept to group communities based on
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context values (for instance the location). We call
Space of Communities (SoC) a set of communities
hosted in a same workspace. Distributed communities
can also be realized since communication in-between
workspaces is possible.
As previously mentioned, we use RDF triples to
model contents within communities, users’ profiles,
and services. This RDF implementation conforms to
open linked data models. We use Sesame repositories
(Broekstra et al., 2002) within artifacts to store and
access these triples. Services are built on top of Web
services. Details of the framework architecture are
described in the next subsections and summarized in
figure 7.
4.2 Implementation of Users Profiles
The user profiles are implemented as described in the
data model (see 3.1) under the form of RDF triples.
Each user’s avatar is composed of an artifact (Pro-
file artifact) and 3 agents (Profilor, Space Detector,
Participator). The Profile artifact is used for access-
ing a Sesame repository which contains triples about
context information, information topics and friends as
defined in 3.1. The Profilor agent lets users manage
their profiles. The Space Detector agent is a contex-
tual detector of SoCs. The Participator agent is cre-
ated when a SoC is detected. It allows the user to join
or create communities: the agent filters the informa-
tion received from the SoCs, depending on the user’s
topics of interest, context and goals and notifies the
user. This process is privacy-aware since no personal
data is transmitted to the SoC. Notice that as the user
profile is unique, avatars of a single user interact to-
gether to merge data and keep up to date.
4.3 Implementation of Communities
and their Services
As stated before, Cartago artifacts are used to imple-
ment communities. Once a community is created,
a community artifact allows the access to a Sesame
repository. It stores the data of this community:
shared contents, roles, services (see 3.2). A dedicated
SoC artifact manages the list of communities and the
list of services the SoC holds.
Services interfaces are also implemented as arti-
facts. They provide a set of methods for querying (us-
ing SPARQL), adding, deleting triples in the commu-
nity repository. The availability of a given service in
a community is decided by the community creator.
4.4 Smart City Scenario and Discussion
Our system can be used for the following scenarios.
A city has implemented in the most populated quar-
ters the ad hoc context-aware community platform
and has settled several spaces of communities with
different web services. A ride sharing service and
a question/answer service are among these. Opera-
tions of the ride sharing service are: declare an offer,
search for daily rides, and book for a ride. The ques-
tion/answer service has two main operations: search
a query and get answers; and read a query and give
an answer. This second service is implemented at the
train station.
As a committed citizen, Alice wants to develop
daily ride sharing in her close area. She has learned
the existence of the community platform and decides
to register and to create her avatar (stored on her de-
vice). She creates a community with ”ride sharing”
as topic, the street name as the context for launching
the services, and she selects operations from the ride
sharing service. John is searching for ”car ride” on
the platform. Since he leaves close to Alice (same
context), and thanks to the semantic analysis (same
content), the platform proposes him to join Alice’s
community.
Bob and Colin have joined the platform. They of-
ten wait in the train station and have learnt that the
train company has created a question/answer com-
munity. Bob loves winning points every day in an-
swering questions asked by other participants. Colin
loves asking questions about subjects he wants to
learn about.
The scenarios illustrate the kind of implementa-
tions our data model can support for smart city ap-
plications. Users can manage their profiles on their
mobile devices, their avatars can collect context data,
and pieces of information can be gathered or deliv-
ered automatically following their goals. Spaces of
Communities (with their artifacts, data stores and ser-
vices) can be hosted by servers in specific places such
as museums, cinemas, stores, campus, points of in-
terest... Communities with ad hoc services can be
created by users with no specific knowledge in com-
puter science, thanks to the use of the semantic web
technologies. Users can join communities and thus
receive or deliver information.
Compared to other approaches, our approach pro-
poses a user profile which is unique and which con-
tains context values from the user’s avatar. It can
be used in all Spaces of Communities. The models
for user profile, context and data are open and com-
pliant with the Semantic Web technologies. Knowl-
edge items are RDF triples contained into URIs. This
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596
Figure 7: Framework architecture.
opens our system to an unlimited amount of knowl-
edge, as it follows the open linked data principles and
technologies. Following such standards also allows
to include other value added services ; for example
reasoning on data can improve the user’s experience
with better recommendations. Finally, services can be
added into Spaces of Communities and they can be
easily included by end-users in the communities they
create. No programming skill is required : the use
of standards for the semantic description of services
allows their inclusion with a high level of interaction
with end users. Different types of communities can
be created, using different services according to the
communities needs. The semantic description of the
avatar context also allows the use of such information
by services ; they can adapt their behavior to the end-
user context of usage.
5 CONCLUSION
The design of ad hoc context-aware communities typ-
ically addresses smart cities scenarios. We have pro-
posed a model and an implementation which rely on
the Semantic Web approach. Our approach can be
useful in many different places and for many scenar-
ios. Our data model can be easily extended to suit spe-
cific needs, and various services can be used by end-
users. Cities, shops, malls, museum, cinemas should
be interested by the approach. In the near future, our
next steps will consist of building a more appropriate
and illustrative user interface for end-user, and of de-
veloping new services which will illustrate different
kinds of communities that could be initiated by users
in different places.
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