Towards Ontology-based Context Aware Mobile Social

Networks

Maha Maalej

, Achraf Mtibaa

and Faïez Gargouri

MIRACL Laboratory

Higher Institute of Computing and Multimedia, University of Sfax, Sfax, Tunisia

Higher Institute of Electronics and Communication, University of Sfax, Sfax, Tunisia

Abstract. Due to the increasing progress of context-aware computing, we ex-

pose the importance of using context-aware mobile technologies to access so-

cial networks. Exploring knowledge in the social networks is a challenge

caused by the exploding size of data in these networks. Thus, using ontologies

better fits this challenge. We propose, in this paper, an approach to combine

these technologies (context, mobile and ontology) together to have a contextual-

ized ontology helping to assist a mobile user in his information retrieval from

the social networks. We end up by giving emphasis to our future works.

1 Introduction

Social networking provides progress, particularly in communication and self-

expression. Since then, millions of people connect to social networks. In fact, users

find a space for sharing information and can follow the news of friends and family.

This space is proved in social networking sites. The emergence of social networking

sites allows companies to promote their products and services. Social networking sites

can easily group users by their information. The publicity is much easier for compa-

nies because they can target users. Job seekers and those who want to make their

online promotions have also used social networking as a means to achieve their goal.

Social networking applications are changing the way of communication by using

user’s context-information. For example micro-blogging has become a smart way of

conveying the current situation and activity by using user context. There is currently a

significant difference between using social networking applications on a static com-

puter compared to a mobile device, even if current mobile devices are powerful and

have good connectivity. The difference is primarily related to the mobility aspect

since the user contexts may change more frequently and the user may not be able to

interact with the mobile device.

Researchers and industry are oriented toward the use of social networks via mo-

bile technology, given the exponential growth of mobile devices. Conversion to mo-

bile version enables customers to a company to benefit from their expertise and

Smartphone instant access to the services of this company. A multitude of benefits

characterizes mobile devices. Certainly, they combine practicality, ergonomics and

simplicity. They are also powerful and allow easy and instant accessibility to infor-

mation. Then, these mobile devices enable instant access to social networks and news.

Maalej M., Mtibaa A. and Gargouri F..

Towards Ontology-based Context Aware Mobile Social Networks.

DOI: 10.5220/0004607200900096

In Proceedings of the 2nd International Workshop on Web Intelligence (WEBI-2013), pages 90-96

ISBN: 978-989-8565-63-1

 2013 SCITEPRESS (Science and Technology Publications, Lda.)

For Smartphones with Android, they provide easy access to social networking sites

and good integration with Google products such as Gmail and Google Maps.

Knowledge representation is an issue troubling and confusing because of the prob-

lems that encounter it. Social networks contain a lot of personal information about

users (name, date of birth, etc.) as well as information on their friendship and their

interests. Many works are required to represent this information by graphs. Fan [7]

proposed a framework of preserving the query graph compression, which preserves

only the information needed to answer a certain query class of choice for users. Some

research has used the RDF graphs to model semantic social networks. Erétéo [6] pro-

posed a framework to exploit directly the RDF representations of social networks by

using the semantic search engines on the Web. Other researchers, such as Corby [4]

used the SPARQL query language to find paths between semantically related to RDF

resources based on graphs.

A whole based on graphs theory is used. Graphs allow structuring concepts and re-

lations between them. Moreover, they allow better visualization. However, the graphs

do not maintain the semantics of the concepts they represent. They do not formalize

the information contained in these social networks. In contrast, ontologies utilization

keeps the semantic relationships between concepts with a better knowledge represen-

tation. Indeed, ontologies are used for the specification of concepts and relationships

associated with a given domain. Social networks are composed initially of entities and

relationships. Thus, domain ontologies can represent these entities and relationships.

Ontologies do not allow modeling conflicting information and the validity of the in-

formation encoded by reasoning. In addition, ontologies can infer new information

through the inference.

Fig. 1. State-of-the-art study’s presentation.

The question that remains how the context is detected in social networks and how

we can represent the knowledge, extracted from the social networks, by ontologies.

We expose our state-of-the-art study in the figure 1. In this figure we present mobile

users who interact with social networks. In their interaction, they are in contact with

contextual information. These information are produced by two sources. The first

source is the mobile phone which has some features like the used platform or screen,

user’s location, etc. The second source is the information about the user itself as his

profile, his friends list, his comments, etc. In the objective to represent the knowledge

extracted from the social network, we use ontologies. By means of ontologies, we

have three steps to reason about the extracted knowledge: knowledge extraction,

knowledge structure and knowledge reasoning. The ontologies which can be em-

ployed can be a personalized ontology that we create with/without adding an existing

ontology as SIOC and FOAF.

The rest of the paper is organized as follows. Section 2 presents related work con-

cerning the user classifications in social networks, the importance of context in social

networks, the usability of mobile devices in ubiquitous computing and some existing

ontologies to represent social networks knowledge. We propose an approach, in sec-

tion 3, to assist a mobile user in his information retrieval from the social network.

Finally, we talk about some of our future works and draw conclusion in section 4.

2 Related Work

A social network consists of people or groups connected by a set of social relation-

ships, such as friendship, co-working or information exchange [8]. Lately, social net-

works have become an important mean of communication and interaction between

people over the Internet. They provide many services offered online: email, instant

messaging, file sharing, etc. Social networks are now being used as well in academia

[17] and business communication not only in free time. A common property of Web

2.0 technologies is that they facilitate collaboration and sharing between users with

low technical barriers on sites and with a limited amount of information. The basic

features of a social network are profiles, friend listings, and commenting, often along

with other features such as private messaging, discussion forums, blogging, and me-

dia uploading and sharing [5]. A user profile, in the social network context, is a col-

lection of personal data associated with a specific user. A user profile can store the

user's interests, gender, birthday, religious beliefs, and other characteristics of the

user. In the next sub section, we present some classifications of social networks’

users and the importance of context in social networks.

2.1 Users Classification in Social Networks

A first classification split the social networks into two parts, i.e. users and the contents

produced by them. If we take a closer look at the users, it is natural to view it as a

combination of consumer and producer from a marketing perspective. Consumer re-

fers to the individuals who only read or watch but never post anything. Some studies

also call them lurkers [16]. Producer encompasses creation and publication of one’s

personal contents such as text, images, audio, and video. Furthermore, some studies

go deep into the classification of users in online environment.

Another classification, is done in Porter’s Funnel Model, identified four types of

users which are interested readers, first-time users, regular users, and passionate users

based on the extent of user’s participation [15].

Nakakoji defined users’ roles in social networks communities as classified in 8

groups including: passive user, reader, bug reporter, bug fixer, peripheral developer,

active developer, core member, project leader [14].

Mao used the data mining techniques to illustrate the user’s categories in online

community as reader, learner, responder, contributor, and leader [13].

2.2 The Importance of Context in SN

Brézillon [2] enumerates the importance of using context in social networks. He un-

derlines the importance of context explicitation to provide a global view of the main

aspects of social networks. First, the explicit consideration of contexts could improve

notably the collaborative work processes in an enterprise. Secondly it is interesting to

simultaneously consider the paradigms of context and social network. Thirdly, differ-

ent types of context account for the flux of information between groups as well as

inside each group are shown.

Joly [10] demonstrates the opportunities offered by the use of context in social

network. First, it is interesting to leverage context information to assist sharing of

information such as get tips from friends when travelling (context: location). Second-

ly, it is practical to use the context information to assist contact management and

awareness. This is due to that context-awareness can improve the social networking

experience by bringing more relevance in social awareness towards more effective

communication.

After exposing the importance of context in social networks, we present the use of

mobile platforms having the ability to detect the context. Smartphones are a new trend

of mobile phones. They are rapidly becoming the central device in people’s lives.

Importantly, today’s Smartphones are programmable and come with a growing set of

cheap powerful embedded components. Now phones can be programmed to support

new sensing applications [11] such as sharing the user’s real-time activity with friends

on social networks keeping track of a person or monitoring a user’s well being.

2.3 Using Mobile Device in Context-aware Computing

The use of Smartphones is growing at an unprecedented rate and is projected to soon

pass laptops as consumers’ mobile platform of choice. The proliferation of these de-

vices has created new opportunities for mobile researchers. The recent years have

seen an enormous growth in the popularity and visibility of Smartphones. Some re-

searchers use the profile of the mobile user to know his context. In this context, Li

[12] proposes semantics-based mobile social network, a framework of a fully func-

tional mobile ad hoc social network that incorporates semantics of users’ social data.

This framework provides effective and efficient solutions to social network construc-

tion, semantics-based user profile matching, etc. Another element which can contrib-

ute to context-aware computing is the location of the mobile user. There are many

Smartphone platforms such as Android, iPhone, etc. As the technologies of these

platforms continue to improve and used in large numbers of devices, location sensing

in mobile devices will undoubtedly become ubiquitous. Also, it has been shown, that

context is useful at different levels within a mobile device. At systems level, it can be

exploited for example for context-sensitive resource and power management. At ap-

plication level, context-awareness enables both adaptive applications and explicitly

context-based services. And at the user interface level, the use of context facilitates a

shift from explicit to implicit human-computer interaction, toward less visible if not

invisible user interfaces [18].

2.4 Existing Ontology to Represent Social Networks Knowledge

The Semantic Web is an extension of the current Web. It well defines the meaning of

information, better enables computers and people to work in cooperation [1] and pro-

vides required representation mechanisms for portability between social media sites.

An ontology, which is a semantic web technology, is defined by Gruber as “a shared

and common understanding of a domain” [9]. Therefore, we use ontology to represent

user and resource profile. The ontology-based representation is more expressive and

less ambiguous [12]. In addition, the ontology provides formal, machine-executable

meaning on the concepts. Moreover, ontology standards support inference mecha-

nisms that can be used to enhance semantic matching.

The FOAF initiative provides a way to represent social network data in a shared

and machine-readable way, since it defines an ontology for representing people and

the relationships that they share. While the SIOC project was initially established to

describe and link discussion posts taking place on online community forums such as

blogs, message boards, and mailing lists. By using agreed-upon Semantic Web for-

mats like FOAF and SIOC to describe people, content objects, and their connections,

social media sites can interoperate and provide portable data by appealing to some

common semantics [3]. As discussions begin to move beyond simple text-based con-

versations to include audio and video content, SIOC has evolved to describe not only

conventional discussion platforms but also new Web-based communication and con-

tent-sharing mechanisms.

3 Ontology-based Context-aware Mobile Social Networks

We propose an approach in order to assist the mobile user in his search on social net-

work which is presented in figure 2. This approach is composed of five steps. First,

we extract knowledge from social network. In this context, to achieve this step, we

use a tool and/or an API associated to the chosen social network. This step allows

extracting diverse form of knowledge explicit, implicit, contextual and non contextu-

al. Second, we will process this amount of knowledge which contains additional in-

formation not useful for our work to keep only the wanted data.

After that, we build an ontology from this processed data to profit from the ad-

vantages of sharing knowledge and keeping semantics of ontology construction. The

ontology will contain the main concepts of social network, their properties, their rela-

tionships and some axioms controlling the structure of the ontology. This step is fol-

lowed by contextualizing the constructed ontology. Indeed, detecting the mobile us-

er’s context is necessary to assist him in searching persons or services in the chosen

social network. The next step consists of comparing the different user profiles and the

contextual information of mobile user. This comparison is achieved by using tools

and algorithms to choose the best result of the user query. The last step permits assist-

ing the user, by mobile interface. The result should suit the mobile performance (size

of the screen, size of memory, etc).

Fig. 2. Our proposed approach to build an ontology-based context-aware mobile social net-

work.

4 Conclusions and Future Works

During this paper, we exposed a state-of-the-art concerning the combination of differ-

ent technologies which are social networks, context-aware computing, mobile devices

and ontologies. First, we demonstrated that the Smartphones, due to their mobility,

can contribute to more access to social networks. Then, we showed that managing

context, in these devices, is necessary to improve their capabilities. Furthermore, we

present some existing ontology which represents a means that permits to preserve

semantics and formalization of social networks knowledge. Afterward, we introduced

our proposed approach to assist the mobile user in his information retrieval from the

social network. In our future works, we intend to contextualize our ontology through a

method that extracts information from a social network using ontology.

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