=Paper=
{{Paper
|id=None
|storemode=property
|title=Towards Semantic Profiling in Social Networks
|pdfUrl=https://ceur-ws.org/Vol-915/paper_9.pdf
|volume=Vol-915
|dblpUrl=https://dblp.org/rec/conf/invit/DAgostino012
}}
==Towards Semantic Profiling in Social Networks==
https://ceur-ws.org/Vol-915/paper_9.pdf
Towards Semantic Profiling in Social Networks
Gregorio D’Agostino, Antonio De Nicola
Computing and Technological Infrastructure Lab,
ENEA: Italian National Agency for New Technologies, Energy and Sustainable Economic
Development,
Via Anguillarese 301 - 00123, Rome, Italy
gregorio.dagostino@enea.it, antonio.denicola@enea.it
Abstract. The growing available implicit knowledge about people in social
networks is fostering a new generation of advanced services centered on the ac-
tual needs of persons. Precondition to them is to provide an explicit formal
specification of what is known about people taking part in the social network.
This specification should be in the terms of a shared conceptualization of one or
more domains of interest related to the social network scope. Here we propose a
method to provide semantic profiles of people taking part in a social network
concerning a specific domain of interest. Our method is an inference process
based on the topological structure of the relationships between people, on exist-
ing explicit interests, and on the assumption that each person has own beliefs
and is partly influenced by friends. In particular, the introduced approach allows
estimating a semantic profile of a newcomer, i.e., a new person joining a social
network, and how it will evolve over time.
Keywords. Social Networks; Semantic Social Networks; Semantic Profiling.
1 Introduction
Social Networks (SN)s collect a huge amount of growing implicit knowledge about
people and domains of interest. General purpose SNs (e.g., Facebook, Twitter) collect
information on several domains (e.g., music, movies, literature, travels) whereas do-
main specific SNs, e.g., anobii and LinkedIn, collect information on specific topics
(e.g., books for anoobi and job and careers for LinkedIn).
Organizing and managing such knowledge may allow definition of a new genera-
tion of services focused on specific users’ needs. Examples are services devoted to a
better organization of human competencies in the enterprise sector; marketing ser-
vices to promote new products and services; and alert services to provide useful or
necessary information to citizens, private enterprises, institutional operators, decision
makers, and possibly other entities.
Precondition to these services is harnessing implicit knowledge at the basis of a SN
(i.e., SN knowledge) and to provide it in an explicit form. SN knowledge concerns
people, i.e., their relationships, their attitudes, their interests, their needs, and their
activities, and, for domain specific SNs, the addressed SN sector.
In this paper we propose a first step to provide an explicit specification of SN
knowledge. We focus on providing a semantic specification of interests (i.e., semantic
profile) related to people taking part in a SN. In particular, we propose a method to
estimate the semantic profile of a new person joining a social network and its evolu-
tion during time. Our method is based on the topological structure of people relation-
ships, on existing explicit interests and on the assumption that each person has her/his
own beliefs and is partly influenced by her/his friends.
The rest of the paper is organized as follows. First we introduce a formal definition
of a social network. Then we describe an incremental approach to specify a domain of
interest and we present our definition of the interest endowed network as a bipartite
graph. Then we propose our method to estimate the evolution of a semantic profile
and a consequent estimated welcome profile related to a newcomer. Finally, related
work in the area and conclusions end this work.
2 Social Network
A social network consists of a community of people linked together with some
kind of relationships (e.g., friendship, coauthorship, working together with). It can be
represented as a directed graph SoN =(P,F) (Figure 1), where the set P of nodes pi
represents people P={p1, p2, …, p|P|}, and the set F of links fj,k represents friendship
relationships between person j and person k as ordered pairs of people F={f1,1, f1,2, …,
f|F|}.
Fig. 1. An Excerpt from a Social Network where (pi)s represent people and (fj,k)s represent
relationships
If the relationship between people is symmetric the graph becomes “undirected”.
There are two ways to account for the symmetric case: one consists into doubling the
links always including two ordered pairs in both orders; while the other consists into
associating a link to a unordered pair. To account for the most general case, the first
approach has been preferred. Most of the considerations presented in the paper apply
to both directed and undirected graphs (i.e., symmetric or non symmetric relation-
ship); when results will depend on such characteristics it will be outlined in the text.
To keep the problem on ground it is worth noting that while Facebook relationship is
intrinsically symmetric, the same does not apply to Twitter.
3 Domain of Interest Representation
According to the modeler’s purpose, a domain of interest can be conceptualized at
different levels of abstraction and represented by means of incremental levels of de-
tails [1]. First, the domain lexicon specifies the terminology used in the domain of
interest. Then the domain glossary allows specifying the definitions corresponding to
the terms. The semantic network allows specifying ontological relationships. Finally,
the ontology provides axioms and the final formalization (e.g., by using the OWL
language [2]).
In the following, as a running example, we consider a community of people work-
ing in the sector of critical infrastructures protection and sharing information related
to such domain.
A domain lexicon DL={t1, t2, …, t|DL|} is defined as the set of terms (ti) used to
characterize a domain of interest.
In the example of the critical infrastructure sector (see above), an excerpt of the
lexicon is reported in the Table 1.
Table 1. Excerpt from the Domain Lexicon concerning Critical Infrastructure Protection
Telecommunications, Transportation, SCADA, Mobile Telecommunications, Fixed Telecommunications,
Rail Transportation, Aviation, Maritime Transportation, Road Transportation, Water, Gas, Electricity
A domain glossary G={g1,…,g|G|} is defined as the finite set of terms belonging to
a domain lexicon DL paired with the corresponding definitions. The pair term and
definition is defined as the glossary entry gi, where
gi = {(ti , defi ) | ti ! DL " defi ! DEF },
and DEF is the set of the definitions of the domain lexicon terms.
In the example of the critical infrastructure protection sector, an excerpt of the do-
main glossary is reported in the Table 2.
Table 2. An Excerpt from the Domain Glossary concerning Critical Infrastructure Protection.
Term Definition
The operation of aircraft to provide transportation. [From WordNet-
Aviation
http://wordnet.princeton.edu]
Maritime Maritime transportation is a means of conveyance of passengers and goods by
Transportation means of watercraft. [Inspired by Wikipedia- http:en.wikipedia.org]
Road
Road transportation is transport on roads of passengers or goods. [From Wikipedia]
Transportation
SCADA (supervisory control and data acquisition) generally refers to industrial
SCADA control systems (ICS): computer systems that monitor and control industrial, infra-
structure, or facility-based processes. [From Wikipedia]
A facility consisting of the means and equipment necessary for the movement of
Transportation
passengers or goods. [From WordNet]
A semantic network SeN can be considered as a simplified version of an ontology
since it consists of a set of concepts and a set of ontological relationships between
them. Consequently, SeN=(C,R), where the set C of nodes ci includes concepts,
C={c1,c2, …, c|C|}, and the set R includes the relationships rk between concept i and
concept j, where
{ }
R = r1 ,r2 ,...,rR = {(ci , c j )} ! C " C .
An example of relationship is similarity (sim) [3], representing how a concept is
similar to another concept.
An ontology O is a formal specification of a shared conceptualization [4] [5]. An
ontology consists of a set of concepts, a set of relationships between them, and a set of
axioms. Consequently, given a finite set of concepts, a finite set R of relationships
established between concepts, and a finite set of semantic axioms Ax, an ontology is
defined as a triple O=(C,R,Ax), where Ax={boolExp1,boolExp2,…,boolExp|Ax|}.
The Table 3 reports an example of axiom in the critical infrastructure protection
sector using Horn Clauses notation [6].
Table 3. Example of Ontology Axiom in the Critical Infrastructure Sector
Axiom. Radioactive materials are transported only by means of special transportation.
Good(_ x) : !RadioactiveMaterial(_ x)
Transportation(_ y) : !SpecialTransportation(_ y)
SpecialTransportation(_ y) : !RadioactiveMaterial(_ x),TransportedBy(x, y)
Fig. 2. A pictorial representation of an excerpt from the Interest Endowed Network concerning
Critical Infrastructures
An Interest endowed Network IeN represents the interests of a community of
linked people. It can be represented as a bipartite graph consisting of a set of nodes N
partitioned in two groups, one representing people and the other the concepts from a
domain of interest conceptualization, and a set of relationships I representing links
between people and concepts only. Consequently, IEN=(P,C,I), where I={i1, i2, …, i|I|},
and
ii = ( p j , ck ) with p j ! P and ck ! C .
An excerpt from the interest endowed network in the critical infrastructures protec-
tion example is reported in the Figure 2.
4 Semantic Social Network
A semantic social network SSN represents, at the same time, the domain of inter-
est, the interests of a community of people, and the relationships among such people. It
can be specified as the union of a semantic network (or an ontology), a social network,
and an interest endowed network. Consequently, SSN=(P,F,C,R,I), where P represents
the set of people; F represents the relationships among people; C represents the set of
concepts; R represents the relationships between concepts; and I represents the interest
of people on concepts.
4.1 Semantic Profiling
We define semantic profiling as the process to associate interests (i.e., concepts of a
domain ontology), related to a specific domain of interest, to a person, in other words
inferring links belonging to the i-th person. The set of interests characterizing a person
pi is defined as her/his semantic profile Spi:
S pi = {ck :( pi ,ck ) ! I }
where
ck ! C , k ! (0, C ) and pi ! P .
In other words the semantic profile of a person is a subset of the interest endowed
network.
The method we propose here allows building a semantic profile of a person pi, i.e.,
newcomer, joining an existing semantic social network SSN, representing a community
of people, a domain of interest, and their interest in such domain. The precondition to
apply this method is to know both the topological structure of the social network and
the semantic profiles of people belonging to it, that is all the SSN. We are looking for a
kind of welcome profiling.
For the sake of simplicity, here, we do not take into account the linked structure of
the ontology and we consider just the concepts per se.
Furthermore, for the newcomer pi, we define the “likelihood” Lpi(ck) as the proba-
bility to be interested in the concept ck.
4.2 Semantic Profile Evolution
Here we estimate how, given a person pi, the probability for he or she to be inter-
ested in a concept ck evolves during time:
1
i
( )
Lp (ck | t + !t) = (1" xi ck ) # Lp (ck | t)+
i Np
# % xij (ck ) # Lp (ck | t)
j
i
p j $N p
i
where xij(ck) is a positive number representing the attitude of a person pi to be in-
fluenced by his or her neighbors (pj) with regard to the concept ck and xi represents the
sum over all j’s that is the total influenceability:
def
" x (c ) ij k
p j !N pi
xi (ck ) = .
N pi
In order to predict the probability of exhibiting an interest in a future time
Lpi(ck|t+Δt), we assumed that a person has her/his own beliefs; this assumption origi-
nates the positive term Lpi(ck|t). As mentioned, we also assume that a person is partly
influenced by people he or she interacts with; this originates both the negative term,
-xi(ck).Lpi(ck|t) (representing the negative influence of the friends on the term ck), and
the positive following term (representing the positive influence of the friends on the
1
term ck): ! # xij (ck ) ! Lp (ck | t) .
Np p j "N p
j
i
i
If we assume that the interest in any concept experiences the same influence from
friends, the xi value does not depend on concept.
When xi=0 we are dealing with a person that is not influenced by other people and
always keeps his or her own opinions. On the other hand, when xi=1 the person is total-
ly bailed out by friends.
To extract a semantic profile from the L 's, we may assume that a person pi is in-
terested in a concept ck if Lpi(ck|t)>Lt, where Lt is a predefined threshold. Consequent-
ly:
{
S p (t) = ck :( pi ,ck ) ! I ! Lp (ck | t) > Lt .
i i
}
4.3 Welcome Profiling
Once a person joins a social network, it may happen that he or she does not have
time or does not desire to express formally interests; therefore the only information
available is contained in her/his links with other people. For this reason, in order to
build a welcome profile, we omit the unknown part concerning past personal interests
in the formula to estimate Lpi(ck) and we just consider the part concerning the influence
of the group of people in his or her neighborhood (assuming there is at least one person
in the group and, consequently, N pi ! 0 ):
1
Lp (ck | t + !t) =
i Np
( )
" $ xij ck " Lp (ck | t) .
j
p j #N p
i i
In practice, we are estimating the interest of a newcomer as just the average of the
interests of his or her friends. Alternatively, we can assume the newcomer to have an
“a priori” average interest L0pi(ck) and provide a welcome profile based on its expected
evolution:
1
Lp (ck | !t) = (1" x ck ) # L0p (ck )+
( ) ( )
# % xij ck # Lp (ck | t = 0).
i i N p p j $N pi j
i
Please note that we consider here t=0 as the starting time of the observation period
of the social network.
Furthermore, as a first approximation, we can assume that the friend’s contribution
is unitary, Lpj(ck|t)=1, if her/his interest on the concept ck is declared, or null otherwise,
Lpj(ck|t)=0. The “a priori” interest L0 may result from analysis of some larger social
network or can be provided by other modeling of the system.
5 Related Work
Business and social experts [7] recognize the growing importance of the new gen-
eration of web sites both for their implications on marketing and for the impact on the
societal and political life.
A list of new generation of methodological approaches fostering the potential of
social networks is presented in [8]. Examples of these methodologies are novel ap-
proaches to study viral marketing [9] and mechanism to govern social influence [10].
Merging social network analysis and semantics-based methods is a new research
approach recently used with promising results [11] [12] [13] [14]. With these works we
share the use of a conceptual representation of a domain of interest in the social net-
work context.
The importance of semantic profiling, that is the main topic of our paper, is recog-
nized by a growing number of research papers [15] [16]. Semantic profiling is mainly
used to support information retrieval systems. The existing approaches are based on
extracting concepts from a domain ontology and by means of a set of existing docu-
ments. While their approach employs a domain ontology, we propose a paradigm shift
to estimate semantic profiles including the topology of the human relationships of the
social network.
6 Conclusions
The growing implicit knowledge available in social networks opens several oppor-
tunities to develop a new generation of advanced services tailored to user characteris-
tics. Semantic profiling is the process to add a semantics-based description of a person.
The former is a precondition to foster the desired intelligent services.
In this paper we have proposed a novel approach to model the evolution of seman-
tic profile in a social network and to provide a newcomer in a social network with an
inferred welcome profile. Our approach is based on the relationships’ topology of the
social network and on assumption that each person carries his or her own beliefs
whilst being partly influenced by friends.
Finally, the present paper is a preliminary theoretical work that is currently under
ongoing validation through a large set of data related to a widespread social network.
As possible applications, we envisage usages in the competencies management and in
the marketing sectors.
References
1. De Nicola A., Missikoff M., Navigli R., A software engineering approach to ontology
building, Information Systems, Volume 34, Issue 2, pp. 258-275, April 2009.
2. Antoniou G., van Harmelen F., Web ontology language: Owl, in: S. Staab, R. Studer
(Eds.), Handbook on Ontologies, Springer, pp. 67–92, 2004.
3. Lin D., An Information-Theoretic Definition of Similarity, In Proceedings of the 15th In-
ternational Conference on Machine Learning, Morgan Kaufmann, pp. 296--304, 1998.
4. Gruber T. R., A translation approach to portable ontology specification, Knowl. Acquis. 5,
pp 199–220, 1993.
5. Borst W. N., Construction of engineering ontologies for knowledge sharing and reuse,
University of Twente, Enschede, The Netherlands, 1997.
6. Lloyd JW. Foundations of Logic Programming (2nd edn). Springer-Verlag: Berlin, 1987.
7. D. Tapscott and A. D. Williams, Wikinomics: How Mass Collaboration Changes Every-
thing, New York, Penguin, 2007.
8. Staab S., Domingos P., Mika P., Golbeck J., Ding L., Finin T., Joshi A., Nowak A., and
Vallacher R. R., Social Networks Applied. IEEE Intelligent Systems 20, 1, January 2005.
9. Domingos P., Mining Social Networks for Viral Marketing, IEEE Intelligent Systems,
Vol. 20, No. 1., pp. 80-82, 2005.
10. Novak A., Vallacher R. R., Information and Influence in the Construction of Shared Reali-
ty, IEEE Intelligent Systems, Vol. 20, No. 1., pp. 90-93, 2005.
11. Jung J., Euzenat J., Towards Semantic Social Networks. In Proc. of ESWC 2007, LNCS
4519, pp. 267–280, 2007.
12. Mika P., Social Networks and the Semantic Web: The Next Challenge, IEEE Intelligent
Systems, Vol. 20, No. 1., pp. 82-84, 2005.
13. Bojrs U., Breslin J. G., Finn A., and Decker S.. Using the Semantic Web for linking and
reusing data across Web 2.0 communities. Web Semant. 6, 1, February 2008.
14. Kinsella S., Passant A., et al.. The Future of Social Web Sites: Sharing Data and Trusted
Applications with Semantics, Advances in Computers, Vol. 76, Is. 09, pp. 121-175, 2009.
15. Bhatt M., Rahayu W., Prakash Soni S., Wouters C., Ontology driven semantic profiling
and retrieval in medical information systems, Web Semantics: Science, Services and
Agents on the World Wide Web, Volume 7, Issue 4, pp. 317-331, December 2009.
16. Zhuhadar L., Nasraoui O., and Wyatt R.. Dual representation of the semantic user profile
for personalized web search in an evolving domain. In Proc. of the AAAI 2009 Spring
Symposium on Social Semantic Web, Where Web 2.0 meets Web 3.0, pp. 84–89, 2009.