UCASFUM: A Ubiquitous Context-aware Semantic Fuzzy User

Modeling System

Hilal Tarakci and Nihan Kesim Cicekli

Middle East Technical University, Inonu Blvd., 06531, Ankara, Turkey

Keywords:

User Modeling, Ubiquitous User Model, Social Web Mining, User Proﬁles.

Abstract:

In this paper, we propose a ubiquitous user modeling system which illustrates different aspects of the indi-

vidual’s interests and his/her current and future context. The user model is constructed by aggregating and

semantically enhancing the partial proﬁles obtained by mining socially enhanced online traces of the user on a

regular basis. Those traces include actions performed and relationships established in the social web accounts

in addition to the local machine traces such as bookmarks and web history. The semantical enrichment process

consists of two phases: constructing an overlay model by using concepts and hierarchical information from

external knowledge bases and creating links from the constructed user model concepts to supported ontolo-

gies. The former phase outputs a semantically enhanced user model whereas the latter enables interoperability

between applications which use the proposed system for personalization. Moreover, fuzzy membership val-

ues are computed for each interest and context item in the user model. In order to model the semantically

enhanced user proﬁle and represent fuzziness values, fuzzy hypergraph is used as data structure. Fuzzy hy-

pergraph representation enables extraction of partial user proﬁles in the requested domains besides answering

user modeling queries such as the degree of the user’s interest for the given concepts. By extracting partial pro-

ﬁles by specifying domains, the proposed system can be used for personalization purposes in multi application

environments.

1 INTRODUCTION

With the advent of Web 2.0, users are allowed to ac-

tively participate in the web by creating content and

interacting with each other by means of social net-

working and tagging platforms (Silva et al., 2008).

Thus, the social web structures which link people to

several concepts and to other users has emerged. The

large scale data created in Web 2.0 reﬂects the in-

terests and preferences about the content contributors

and is an invaluable data source for personalization

purposes.

The goal of Web 3.0(Lassila and Hendler, 2007) is

to close the gap between reality and virtual world by

personalizing the web. In order to achieve this goal,

Web 3.0 focuses on the individuals and supports per-

vasive and ubiquitous computing. Ubiquitous appli-

cations should be capable of running on different de-

vices and should be aware of the preferences of the

individual and the context. An example of a ubiqui-

tous application scenario is presented in (Carmichael

et al., 2005). In this scenario, there is a locator ser-

vice which connects to the user if he is available or

declares the user is busy. In order to achieve this task,

the service is able to sense the location of the user,

and whether he is actively working on the computer

in addition to being aware of his priorities and distin-

guishes whether the situation requires to interrupt his

work.

In order to support such use cases, a ubiqui-

tous and context-aware semantically enriched user

model is essential. The model should be interoper-

able amongst applications, otherwise each applica-

tion has to manage its own user proﬁle, thus increas-

ing the computation costs. In this paper, we propose

such a model which illustrates different aspects of the

individual’s interests and preferences besides his/her

current and future context. The user model is con-

structed by aggregating and semantically enriching

partial proﬁles obtained by mining socially enhanced

online traces of the user on a regular basis. Those

traces include actions performed and relationships es-

tablished in the social web accounts in addition to the

local machine traces such as bookmarks and web his-

tory.

The semantical enrichment process consists of

278

Tarakci H. and Kesim Cicekli N..

UCASFUM: A Ubiquitous Context-aware Semantic Fuzzy User Modeling System.

DOI: 10.5220/0004169702780283

In Proceedings of the International Conference on Knowledge Engineering and Ontology Development (KEOD-2012), pages 278-283

ISBN: 978-989-8565-30-3

 2012 SCITEPRESS (Science and Technology Publications, Lda.)

two phases: constructing an overlay model by us-

ing concepts and hierarchical information from ex-

ternal knowledge bases and creating links from the

constructed user model concepts to supported ontolo-

gies. The former phase outputs a semantically en-

hanced user model whereas the latter enables interop-

erability between applications which use the proposed

system for personalization. Moreover, fuzzy member-

ship values are computed for each interest and context

item in the user model.

The semantically enhanced user proﬁle which is

enriched with fuzziness values are stored by utilizing

fuzzy hypergraph data structure. Fuzzy hypergraph

representation enables extraction of partial user pro-

ﬁle in the requested domains and output formats be-

sides answering user modeling queries such as the de-

gree of the user’s interest for the given concepts. By

extracting partial proﬁles by specifying domains, the

proposed system can be used for personalization pur-

poses in multi application environments.

2 RELATED WORK

In our study, we aim to exploit online traces of the

user on social networking and tagging environments

in order to construct the user model. Moreover, we

propose to mine the social web in a context-aware

manner and compute fuzziness values for the discov-

ered information about the individual during aggrega-

tion and semantic enrichment of partial proﬁles which

are obtained from different knowledge sources. The

constructed user model is able to extract partial user

proﬁles for speciﬁed domains in supported ontology

formats in order to provide personalization for multi

application environments. Therefore, our research

is related to cross system personalization, ubiquitous

user modeling process for multi application environ-

ments and fuzzy user modeling.

Cross system personalization is formulated in

(Mehta, 2009) and proved to be effective in cold start

problem in addition to providing a more robust user

proﬁle. The nature of individual user proﬁles dis-

tributed on the social web is analyzed in (Abel et al.,

2011). In our study, we not only consider explicitly

stated form based information in social networks, but

also activities performed such as sharing or comment-

ing on a video about a certain topic and clicking the

‘like’ button on a sports team page etc. Moreover, we

consider check-in declarations on Facebook proﬁles

for current context of the user and events for his/her

possible future context. A generic user modeling li-

brary for the social semantic web which allows for

generating proﬁles that summarize the given stream

of messages according to domain and application spe-

ciﬁc requirements is proposed in (Gao et al., 2011).

Similarly, we aim to tailor the constructed user model

in accordance with the needs of the requester appli-

cations. Furthermore, we intend to manage whole life

cycle of an individual’s user model by considering not

only the construction of the proﬁle but also the neces-

sary information updates to the proﬁle.

In a multi-application environment, there are two

scenarios of constructing and consuming user pro-

ﬁles. In the ﬁrst scenario, each application may con-

struct a partial user model and the challenge is reusing

built partial user models amongst applications. The

second scenario which we adopt, separates the user

model constructor and consumer applications. (Vi-

viani et al., 2010) classiﬁes user modeling approaches

for multi application environments as standardization

based and mediation based user modeling. In stan-

dardization approaches, all participating applications

in the environment which are consuming the proﬁle

are required to support the same user model. We pro-

pose a hybrid solution by constructing a user model

which is dynamically mapped to several well known

ontologies during construction phase. The proposed

user model is capable of exporting the required por-

tion of the proﬁle partially in the form of the ontology

supported by the consumer application.

In (Kavcic, 2004), the uncertainty in the user’s

knowledge description is dealt with a fuzzy user

model in adapting educational hypermedia domain.

The uncertainty arises from vague boundaries be-

tween known and unknown concepts whereas in our

study the uncertainty is the problem of determin-

ing set memberships of the user proﬁle items. In

(Vanekov and Vojts, 2009) partial preferences of the

user are combined by using monotone aggregation

function and stored in an ontology structure. How-

ever, in our system we are trying to determine the

conﬁdence of the user proﬁle item instead of setting a

preference ordering between proﬁle items.

When the user model is semantically enhanced

and fuzzy membership values are taken into account,

more sophisticated user model structures are required,

since pairwise relations is not able to represent higher

order relations amongst concepts. (Ghoshal et al.,

2009) models folksonomies as tripartite graph struc-

tures.However, tripartite graphs are not able to rep-

resent relations with order 4 or higher. In order to

address this problem, (Tan et al., 2011) models higher

order relations in the social network as a uniﬁed hy-

pergraph and considers recommendation as a ranking

problem on the constructed hypergraph. Inﬂuenced

by this idea we employ uniﬁed fuzzy hypergraph(Roy

H. Goetschel, 1995) structure which is able to model

UCASFUM:AUbiquitousContext-awareSemanticFuzzyUserModelingSystem

279

high order relations naturally in addition to support-

ing applications with different data reliability require-

ments which is determined by fuzzy membership val-

ues.

3 SYSTEM OVERVIEW

UCASFUM system consists of two main components

which are responsible for partial and semantic pro-

ﬁle construction. The former includes submodules

for mining socially enhanced online traces of the user

in order to build separate and independent fuzzy user

proﬁles from each knowledge source, whereas the lat-

ter receives the built partial user proﬁles and activ-

ity streams from social web accounts, and extracts

the current and future context of the user besides

constructing fuzzy hypergraph proﬁle by enriching

the user model semantically by exploiting external

knowledge bases. The context in this work consists

of place and time.

The proposed system is also equipped with a

query engine module which enables extracting par-

tial proﬁles on requested domains in speciﬁed output

formats and answering user modeling queries such as

’To what extent is the user interested in having a vaca-

tion in Switzerland’. Answering this query requires a

fuzziness computation on the model by using vacation

and Switzerland concepts unless ’having a vacation in

Switzerland’ is already explicitly modeled. The sys-

tem components are visualized in Figure 1.

3.1 Partial Proﬁle Builders

In the proposed work, the information which is re-

quired in order to construct the holistic user proﬁle

is synthesized from two main sources: (i) distributed

user proﬁles embedded in the social accounts and (ii)

the traces left on the devices of the individual. The

former is the main knowledge source for extracting

user interests and preferences and by analyzing the

social bits and social bytes. A social bit is deﬁned

as an atomic data unit which is acquired from a per-

son’s online social activity and indicates the presence

or absence of a single feature. Assembling social bits

into groups to represent a single unit of information

produces a social byte.For instance, a YouTube video

which is shared on the Facebook page of an individ-

ual is a social byte whereas the attributes about the

video such as its genre, number of likes or comments

are its social bits. The latter knowledge source which

is the devices owned by the individual is optional and

requires the user to install a client application to ana-

lyze his web usage data and bookmarked web sites.

In the proposed system Facebook, Twitter and

LinkedIn social web accounts are mined besides the

user’s personal and work pc and his/her smart phone.

However, the system can be easily scaled for other

social web accounts or personal devices.

3.2 Semantic Proﬁle Builder

The semantic proﬁle builder component aggregates

the separate partial user proﬁles constructed by partial

proﬁle builders and processes the incoming activity

streams from the social web accounts of the individ-

ual. This module is responsible for two main tasks:

extracting user’s current and future context and build-

ing the fuzzy hypergraph user model by aggregating

the partial proﬁles.

The context extractor module searches for user’s

check-in declarations in order to determine his/her

past and current context, whereas the events are ex-

ploited to extract possible future context for the indi-

vidual. In this work, place and time information is

used to model context. However, the system can be

modiﬁed to exploit other context such as the weather,

people in vicinity etc.

A hypergraph is the generalization of an ordinary

graph by introducing hyperedges which are nonempty

subsets of the vertex set. Nodes of a hypergraph rep-

resents the entities to be modeled such as users and

concepts in social networking domain. Hyperedges

represent the high order relations between those en-

tities. In this study, we assign the node which speci-

ﬁes the category of other nodes as the type of the hy-

peredge. For instance, a hyperedge connecting a user

with type footballer and nodes Messi and Arda Turan

models the situation that Messi and Arda Turan are

amongst favorite footballers of the user. In a fuzzy

hypergraph, each vertex in the hyperedge is assigned

a fuzziness value in the range [0,1] representing the

reliability of the entity belonging to the relation mod-

eled by the hyperedge.

The fuzzy hypergraph proﬁle builder aggregates

the partial proﬁles in order to construct a holistic user

model. During aggregation process, the raw user in-

terest proﬁles are semantically enriched by construct-

ing an overlay model by using concepts and hierarchi-

cal information from external knowledge bases. The

primary vocabulary used to construct such model is

selected as Wikipedia categories as in (Ramanathan

and Kapoor, 2009) and (Min and Jones, 2011) due to

the maturity of the ontology and its ease of use with

the help of DBPedia. The secondary knowledge base

is Wordnet, and is used when the concept can not be

located under Wikipedia categories.

The semantic enrichment process consists of two

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Figure 1: UCASFUM System Overview.

Figure 2: Raw User Proﬁle for the user Feride.

phases. The ﬁrst phase constructs an overlay model

by using concepts and hierarchical information from

external vocabularies, revises fuzzy membership val-

ues and outputs a semantically enhanced user model.

The second phase creates links from the constructed

user model concepts to supported ontologies enabling

interoperability between applications which use the

proposed system for personalization.

The ﬁrst phase is illustrated with an example. The

simpliﬁed raw user proﬁle which is assumed to be

obtained after aggregation of partial proﬁles is illus-

trated in Figure 2. According to her raw proﬁle,

Feride likes football, Galatasaray which is a Turk-

ish football team, Arda Turan who was a Galatasaray

football player, Lionel Messi who is a famous football

player, tennis, Roger Federer who is a tennis cham-

pion, politics and a Turkish politician Hakan Sukur

who used to be a Galatasaray football player. In or-

der to semantically enhance the example raw pro-

ﬁle, Wikipedia categories are searched in order to

discover a super category for interest items. A par-

tial Wikipedia category tree which spans the interest

items in the sample proﬁle is presented in Figure 3.

Creating other spanning trees for proﬁle items is pos-

sible, since the same item may be categorized under

more than one category in Wikipedia category tree.

The categories that match the example raw proﬁle

items are shaded in the ﬁgure. The constructed se-

mantic user model is as follows:

Hyperedge {

type: Ball Games,

nodes:

Hyperedge {

type: Football

nodes:

Hyperedge {

type: Football Clubs in Turkey,

nodes:

Hyperedge {

type: Galatasaray S.K. (Football Team)

nodes:

Hyperedge {

type: Galatasaray S.K. Footballers,

nodes: Arda Turan, Hakan Sukur }

UCASFUM:AUbiquitousContext-awareSemanticFuzzyUserModelingSystem

281

}

Hyperedge {

type: Association Football Forwards S.K. Footballers,

nodes: Lionel Messi }

}

Hyperedge {

type: Tennis,

nodes:

Hyperedge {

type: List of US Open Tennis Champions,

nodes: Roger Federer}

}

Hyperedge {

type: Society,

nodes:

Hyperedge {

type: politics,

nodes:

Hyperedge {

type: Turkish Sports-person Politicians,

nodes: Hakan Sukur}

}

3.3 Query Module

The query module is responsible for two tasks: (i)

providing extraction of partial user proﬁle for the re-

quested domains in the speciﬁed output format and

(ii) answering user modeling queries. The ﬁrst task

enables personalization of multi application environ-

ments by parameterizing user proﬁle requests in three

dimensions: a list of domains concerning the re-

quester application, the desired output format which

must be one of the supported ontologies by the sys-

tem and a reliability threshold speciﬁed according to

reliability requirements of the application. The fuzzy

hypergraph structure enables extraction of partial pro-

ﬁles on requested domains by using types of hyper-

edges and external vocabularies. When requested

domains match types of hyperedges, those hyper-

edges are sent to the requested application as partial

user proﬁle. For instance, an application which is

about football requests a partial user proﬁle limited

to football domain, whereas a more speciﬁc applica-

tion which sells Galatasaray products wants a partial

proﬁle on Galatasaray. The partial proﬁles which are

sent to the requester applications are hyperedges with

type football and Galatasaray S.K. (Football Team)

respectively.

If a requested domain does not match any of the

hyperedge types, the domain is located on the ex-

ternal knowledge bases and the semantic user model

is searched for the domain’s subcategories. For in-

stance, if the requested domain is sports, no matching

Figure 3: Wikipedia Category Tree for interests of the User

Feride.

hyperedge could be found in the semantic user model

for the sample user Feride. However, when sports

domain is located in the Wikipedia category tree and

subcategories are examined, Ball games hyperedge is

sent as partial user proﬁle.

The proposed system is able to provide partial user

proﬁles in the supported ontologies by using the exter-

nal links constructed during Phase2 of Semantic En-

richment Algorithm. The fuzzy membership values

assigned to each user proﬁle item enables ﬁltering the

user proﬁle items according to their reliability.

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4 CONCLUSIONS

In this paper, we propose a ubiquitous user model for

multi application environments which is constructed

by mining the user’s activities on social web accounts.

Furthermore, we anticipate that computing fuzziness

values, modeling the user in a context-aware manner

and semantically enriching the constructed model will

reinforce the reliability of the user proﬁle. In order

to accomplish this, fuzzy hypergraph data structure,

which naturally represents high order relations and

deﬁnes fuzzy membership values for each element of

hyperedges, is used to model the user. In future work,

we perform extensive analysis on social web mining

methodology by providing several fuzziness compu-

tations, context-awareness and semantic enrichment

approaches in order to evaluate the effect of fuzziness,

semantic enhancement and context-awareness on the

reliability of the ultimate user proﬁle.

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