The growing available implicit knowledge about people in social networks is fostering a new generation of advanced services centered on the actual 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 existing 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.
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 domain 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 generation of services focused on specific users’ needs. Examples are services devoted to a better organization of human competencies in the enterprise sector; marketing services 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 evolution during time. Our method is based on the topological structure of people relationships, 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
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|}.
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 relationship); 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.
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
details [
In the following, as a running example, we consider a community of people working 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. 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 domain glossary is reported in the Table 2.
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) [
An ontology O is a formal specification of a shared conceptualization [
The Table 3 reports an example of axiom in the critical infrastructure protection
sector using Horn Clauses notation [
Good(_ x) : !RadioactiveMaterial(_ x) Transportation(_ y) : !SpecialTransportation(_ y) SpecialTransportation(_ y) : !RadioactiveMaterial(_ x), TransportedBy(x, y)
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 protection example is reported in the Figure 2. 4
A semantic social network SSN represents, at the same time, the domain of interest, 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
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: where
Spi = {ck :( pi , ck ) ! I } 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 probability to be interested in the concept ck. 4.2
Here we estimate how, given a person pi, the probability for he or she to be interested in a concept ck evolves during time:
Lpi (ck | t + !t) = (1" xi (ck )) # Lpi (ck | t) + 1 N # % pj$N xij (ck ) # Lp (ck | t) j where xij(ck) is a positive number representing the attitude of a person pi to be influenced 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: xi (ck ) d=ef pj !Npi
. " xij (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 originates 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 term ck): 1 N pi !
# pj"N pi
xij (ck ) ! Lp j (ck | t ) .
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 totally bailed out by friends.
To extract a semantic profile from the L 's, we may assume that a person pi is interested in a concept ck if Lpi(ck|t)>Lt, where Lt is a predefined threshold. Consequently:
S pi (t ) = {ck :( pi , ck ) ! I ! Lpi (ck | t) > Lt } . 4.3
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 ):
Lpi (ck | t + !t ) = 1 N pi
pj #N pi " $ xij (ck ) " Lpj (ck | t ) .
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:
Lpi (ck | !t) = (1" x (ck )) # L0pi (ck ) + 1 N pi # % xij (ck ) # Lpj (ck | t = 0). pj$N pi
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
Business and social experts [
A list of new generation of methodological approaches fostering the potential of
social networks is presented in [
Merging social network analysis and semantics-based methods is a new research
approach recently used with promising results [
The importance of semantic profiling, that is the main topic of our paper, is
recognized by a growing number of research papers [
The growing implicit knowledge available in social networks opens several opportunities to develop a new generation of advanced services tailored to user characteristics. 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 semantic 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.