A Dynamic and Context-aware Model of Knowledge Transfer and
Learning using a Decision Making Perspective
Evelina Giacchi, Aurelio La Corte and Eleonora Di Pietro
Department of Electric, Electronics and Computer Engineering, University of Catania,
viale Andrea Doria 6, Cittadella Universitaria, Ed. Polifunzionale, 95127 Catania, Italy
Keywords:
Context-Aware, Dynamism, Knowledge Transfer, Knowledge Learning, Decision Making, Social Networks,
Confidence.
Abstract:
All the processes taking place in a social network are characterised by dynamism, complexity and context-
dependence. Processes involving knowledge have these features. The intrinsic characteristic of knowledge is
represented by the value that it can generate in a network, due to its constant and continuous rate of growth. In
a heterogeneous network not all the nodes have similar knowledge levels. Furthermore, not all the connections
have the same importance. In order to consider knowledge as a resource and not as an obstacle, it is admittable
that nodes can decide individually with whom transfer knowledge. Using a context-aware decision making
perspective and considering each single node as a decision maker that has to decide in a particular context
whether accept the transfer or not, it will be helpful to understand how and why certain mechanisms and
behavioural patterns arise.
In this paper, the proposed model considers the process of knowledge transfer as a decision making one, where
each alternative, one of the nodes neighbor that wants to transfer knowledge, has an evaluation on the basis of
two criteria, knowledge distance and confidence. Their values are dynamically updated at each time step on
the basis of the quality of the knowledge transferred.
1 INTRODUCTION
In the era of innovation and technology advance data,
information and knowledge play a central role in any
process regarding the development and the progress
level of a society. The main aim for all the coun-
tries is to become “knowledge societies” in contin-
uous development thanks to the limitless knowledge
growth which generate incommensurable value (Fe-
doroff, 2012). Furthermore, thanks to the evolution
of the Information and Communication Technology
(ICT) there are no limits on when, where and how
knowledge has to be transferred among individuals.
Looking much more in detail, each individual decides
(Guy et al., 2015) and acts within a social network,
characterised by a dynamic, ubiquitous, complex and
context-dependent nature. For each entity (Cioffi-
Revilla, 2013), representing the network node, the
consideration of who is connected to whom as well
as the structure of the network have an important ef-
fect on the type of information passed, on its quantity
and on the efficiency of the process itself (Cowan and
Jonard, 2004). Furthermore, by taking into account
the role of the context, the importance of each single
relation (Barrat et al., 2004) and the structure of the
network itself can vary depending on the considered
context. In fact it is different the level of awareness
held by the single node.
In this paper we consider a process of knowledge
transfer using a context-aware decision making per-
spective (Giacchi et al., 2014) in which, before ac-
cepting or rejecting knowledge from one of its neigh-
bors, a network node judges if its evaluation satisfies
some criteria, i. e. knowledge distance and confi-
dence, and, after that, it decides what to do. If the
process takes place and the receiver node accepts the
transfer, it will perform a control on what it has just
accepted on the basis of three parameters. If the con-
trol result is positive, the receiver node will increase
its confidence in the sender node. On the contrary
case it will decrease its confidence and it will learn
only a percentage of the received knowledge.
The paper is organized as follows. Section 2 gives
a brief overview on knowledge and its typical pro-
cesses and on context-aware applications. Section 3
is the main part of the work, where the whole process
involving knowledge by exploiting a decision mak-
66
Giacchi, E., Corte, A. and Pietro, E.
A Dynamic and Context-aware Model of Knowledge Transfer and Learning using a Decision Making Perspective.
In Proceedings of the 1st International Conference on Complex Information Systems (COMPLEXIS 2016), pages 66-73
ISBN: 978-989-758-181-6
Copyright
c
 2016 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved

ing perspective is explained. The results are shown in
Section 4. Section 5 collects conclusions and reports
future directions of research.
2 RELATED WORKS
2.1 Knowledge and Its Processes within
a Network
Knowledge guides every process in a network. Two of
the main features of the processes that involve knowl-
edge are complexity and dynamism, which are related
to the property of the processes environment itself.
Between the definition of knowledge and informa-
tion there is a substantial difference, even if in some
cases they are used indifferently. Information is com-
pared to a “flow of messages” (Nonaka, 1994) that can
contribute to shape an individual outlook or insight
(Davenport and Prusak, 1998). Knowledge instead
“is a fluid mix of framed experience, values, contex-
tual information, and expert insight that provides a
framework for evaluating and incorporating new ex-
periences and information” (Davenport and Prusak,
1998). In the Knowledge Management field it is also
important to distinguish between two categories of
knowledge: tacit and explicit. Tacit knowledge was
firstly introduced in 1967 (Polanyi, 1967) and it refers
to the knowledge that is difficult to express and trans-
mit because it depends on human and personal quali-
ties of the individual, that make it not easily transfer-
able among individuals (Nonaka, 1994). On the con-
trary explicit knowledge is easily formalized, codified
and transmitted in a formal and systematic language
(Nonaka, 1994; Brown and Duguid, 1991). It can be
found in databases, manuals and documents.
In a network as well as among different individ-
uals, knowledge can be shared, transferred and ex-
changed (Graham et al., 2006). Knowledge shar-
ing corresponds to the provision of information and
know-howof a task among individuals inside and out-
side a group (Cummings, 2004). Knowledge trans-
fer includes two phases: the sharing of knowledge
from a source and its acquisition from a recipient.
Knowledge exchange involves both knowledge shar-
ing through which a source provides knowledge and
knowledge seeking, where a receiver searches knowl-
edge from sources (Wang and Noe, 2010). Sev-
eral works have analysed the processes involving
knowledge in a network by using different perspec-
tives (Lambiotte and Panzarasa, 2009; Tasselli, 2015;
Hatak and Roessl, 2015).
2.2 Context-aware Applications
As previously stated, the third feature of a process in-
volving knowledge is its context-dependance. It is a
consequence of the environment in which each pro-
cess takes place, as for the other two features. Until
now there is not a standard definition of context, but
several attempts have been made. In fact, several and
different definitions are present in the scientific litera-
ture, but most of researchers agree to consider context
as ”any information that can be used to characterise
the situation of an entity. An entity is a person, place
or object that is considered relevant to the interaction
between a user and an application, including the user
and applications themselves” (Abowd et al., 1999).
As a consequence of the information held, different
entities can have, for example, contrasting perception
of the same circumstance. Consequently, if a system
uses context to provide information and/or services to
the user, it can be defined as context-aware (Abowd
et al., 1999). Accordingly, the ability of a system to
discover and to react to changes in the environment
it is in, is defined “context-awareness” (Schilit and
Theimer, 1994).
Nowadays context-aware applications are used in
several fields thanks also to their integration with sen-
sors and geographic information systems. In such a
way, the services that it is possible to provide are more
specific, advanced and cover several sectors (Guer-
mah et al., 2013; Gui et al., 2011).
3 MODEL DESCRIPTION
A model of knowledge transfer is characterised by
three main features i. e. the dynamism, the com-
plexity and the context-dependence. The model pre-
sented in this paper looks at the knowledge transfer
process as a decision making one, taking as reference
points two models reported in the scientific literature
(Cowan and Jonard, 2004; Luo et al., 2015). We as-
sume that the process of knowledge transfer can be
considered as an individual decision making process
where each node, part of a network, is involved in a
process of knowledge transfer. In particular, it has to
decide whether to accept or not knowledge coming
from its neighboring nodes which represent the set of
alternatives. In a first stage, it has been chosen to take
into account only a single process regarding explicit
knowledge, due to its unambiguous and clear charac-
teristics of easy codification and transmission.
For the description of our model we shall use the
following notation:
• N = {n
1
, .. . , n
i
, . . . , n
m
}, a finite set of nodes;
A Dynamic and Context-aware Model of Knowledge Transfer and Learning using a Decision Making Perspective
67

• K = {K
1
, . . . , K
k
, . . . , K
p
}, a finite set of contexts;
• v
K
k
i
(t) =
n
v
K
k
i,1
(t),. . . , v
K
k
i,l
(t),. . . , v
K
k
i,q
(t)
o
, the
knowledge vector of the node n
i
with respect to
the q categories and the context K
k
at time t;
• A
K
k
ij
=
n
a
K
k
ij
o
, the adjacency matrix representing
the network in the context K
k
. a
K
k
ij
= {0, 1} is each
single element which identifies if the link between
nodes n
i
and n
j
is present or not;
• N
K
k
i
=
n
n
j
∈ N : a
K
k
i, j
= 1
o
, the set of nodes linked
to node n
i
in the context K
k
. It represents the set
of alternatives for node n
i
.
As explained in Section 1 one of the context roles is
to characterise and differentiate the strength of each
node’s connection and the structure of the network
itself. In order to do so, we consider the vector of
weights w
K
k
i
=

w
K
k
i,1
, . . . , w
K
k
i, j
, w
K
k
i,m

, where each ele-
ment w
K
k
i, j
represents the strength of the relation be-
tween node n
i
and node n
j
in the context K
k
. w
K
k
i, j
can
be different from w
K
k
j,i
(w
K
k
i, j
6= w
K
k
j,i
). With respect to
the previous models, the decision whether to accept
or not the knowledge offered from another node in
the network is based on two criteria i. e. knowledge
distance and confidence. Each alternative n
j
∈ N
K
k
i
has an evaluation on each of the two criteria. The first
criterion is defined as:
d
K
k
ij,l
(t) = v
K
k
j,l
(t) − v
K
k
i,l
(t) (1)
This distance represents the quantity of knowledge
that node n
i
could receive from node n
j
in the cat-
egory l within the considered context. The knowl-
edge distance can be considered the expression of the
knowledge heterogeneity of the two nodes involved
in the process. If there is a high knowledge gap be-
tween two network nodes (high heterogeneity), node
n
i
could have no gain from the knowledge received
from node n
j
(Luo et al., 2015).
The second criterion is represented by the confi-
dence. In particular, at the moment, we suppose that
the confidence c
K
k
i, j
that the node n
i
has in node n
j
in
the context K
k
is defined as:
c
K
k
i, j
(t) =
w
K
k
i, j
(t) + J
K
k
i, j
2
(2)
where w
K
k
i, j
(t) is the weight that node n
i
gives to the
link with node n
j
. J
K
k
i, j
is the Jaccard similarity (Jac-
card, 1901) i. e. an expression of the concept of ho-
mophily (Lazarsfeld et al., 1954; Di Stefano et al.,
2015), calculated as the ratio of the common neigh-
bors of the nodes n
i
and n
j
to the number of nodes
that are neighbors of at least one between n
i
and n
j
.
The greater the confidence that n
i
has in n
j
, the more
susceptible node n
i
is to learn from node n
j
(Pentland,
2014). In order to ensure that the knowledge transfer
process to take place, the evaluation of alternative n
j
belonging to the set N
K
k
i
in each of the two decision
criteria has to satisfy at the same time this two condi-
tion:
• d
K
k
ij,l
(t) ≤ d, that is the knowledge distance has to
be under a knowledge distance threshold;
• c
K
k
i, j
(t) ≥ c, that is the confidence has to be over a
certain confidence threshold.
Among the set of nodes satisfying at the same time
both conditions related to the two criteria, node n
i
for
each knowledge category will accept knowledge from
the one that can give it the greatest amount of knowl-
edge. The knowledge level of node n
i
in the category
l in the context K
k
will become:
v
K
k
i,l
(t + 1) = v
K
k
i,l
(t) + max
n
j
∈N
K
k
i
((λ
K
k
ij,l
(v
K
k
j,l
(t) − v
K
k
i,l
(t)))
(3)
where:
• v
K
k
i,l
(t) (v
K
k
j,l
(t)) represents the knowledge level of
node n
i
(n
j
) in category l in the context K
k
at time
t;
• λ
K
k
ij,l
represents the absorptive capacity of node
n
i
with respect to the knowledge received from
node n
j
in the category l. In this model, we as-
sume that the value of λ
K
k
ij,l
is strictly related to
the risk attitude of node n
i
(Kahneman and Tver-
sky, 1979). As shown in Figure 1, we assume
that the process of knowledge transfer is located
into the region identified by the red box i. e. the
greater the amount of knowledgereceived by node
n
i
(x
1
< x
2
) the greater its utility is (u(x
1
) < u(x
2
))
but the greater its risk aversion is with the increas-
ing quantity of knowledge that a node n
j
wants to
transfer to node n
i
(Binswanger, 1980; Holt and
Laury, 2002), in order, for example, not to imperil
its security (La Corte et al., 2011).
Hence, the value of λ
K
k
ij,l
will be a function of the
knowledge distance and it can be expressed as:
λ
K
k
ij,l
(t) =
1
exp
d
K
k
ij,l
(t)
(4)
According to this formulation, the values that
λ
K
k
ij,l
(t) can assume are included in the set
h
1
exp
d
;1
i
. In such a way if the values are closer to
1
exp
d
it means that node n
i
is more risk averse and
COMPLEXIS 2016 - 1st International Conference on Complex Information Systems
68

Figure 1: Utility function in the prospect theory.
then it assimilates less knowledge, than a node
that has a value of λ
K
k
ij,c
(t) near to 1 that it assimi-
lates more knowledge.
After that, node n
i
will make a control on the received
knowledge before learning it, that is the evaluation of
its quality on the basis of three criteria (Bukowitz and
Williams, 2000; Suwa et al., 1982):
• Accessibility, defined as the capability for the re-
ceiver node to easily access to the whole knowl-
edge that it has received;
• Guidance, defined as the knowledge property to
be divided into topics or domain in order to avoid
an information overload;
• Completeness, defined as the knowledge property
to contain all the information requested by the re-
ceiver node
If the evaluation of the received knowledge exceeds
the quality threshold in at least two of the three crite-
ria, node n
i
will learn and assimilate knowledge at all.
Furthermore, it will increase the weight and then the
confidence in node n
j
. On the contrary, node n
i
will
learn only the 20% of the received knowledge and its
confidence in node n
j
will decrease. In particular, the
weights will increase or decrease as follows:
w
K
k
i, j
(t + 1) = w
K
k
i, j
(t) ±
q
∑
l=1
d
K
k
ij,l
100
(5)
In the proposed model every network node thinks,
acts and decides in several and different contexts that
are related each other, modifying the measures that
characterise the network. In order to calculate and
analyse this correlation, we consider each context as
a plane of the space and, taking one as a reference
plane, the greater the cosine of the angle between
two planes is the more similar they are, on the con-
trary they are less similar. In Figure 2 the correlation
among contexts is shown. Furthermore, its dynamic
nature is shown, because the reference context and the
position of each plane in the space can vary at differ-
ent time instants.
4 RESULTS AND DISCUSSION
In this section, we analyse the model performance un-
der different simulation hypothesis, considering for
example a scenario in which network nodes have to
accept knowledge, defined in Section 2, from their
neighbors through emails, social networks or via a
face-to-face contacts. We compare the results of
two networks that follow the first one the Erd¨os-
R´enyi model (Erd¨os and R´enyi, 1959) and the second
one the Barab´asi-Albert model (Barab´asi and Albert,
1999). Both networks are characterised by the follow-
ing parameters:
• m = 500, the number of nodes composing the net-
work;
• the number of categories q is set to 5;
• the distance threshold is set to 0.2;
• the confidence threshold is set to 0.4;
• the quality threshold is set to 0.5;
• each knowledge category has a fixed evaluation
on each single quality parameter i.e. accessibility,
guidance and completeness;
• the network configuration does not change over
time i.e. the number and the mutual connections
do not change;
• only one context K
k
has been considered ;
• for the Erd¨os-R´enyi model, we consider a proba-
bility p = 0.3, where p represents the probability
of having a connection between two nodes;
• we suppose that the Barab´asi-Albert model fol-
lows a law of linear preferential attachment.
In the two cases, we use two measures in order to eval-
uate in which manner the two network models per-
form. The two measures are:
• the knowledge percentage held by node n
i
at time
t + T in the context K
k
:
v
K
k
i
(t + T) =
∑
q
l=1
(v
K
k
i,l
(t + T) − v
K
k
i,l
(t))
q· 100
(6)
• the confidence value of each node at time t + T in
the context K
k
:
c
K
k
i
(t + T) =
∑
i6= j
(c
K
k
i, j
(t + T) − c
K
k
i, j
(t))
|N|
(7)
In order to show the dynamism of the proposed
model, considering the Erd¨os-R´enyi network config-
uration, in Figures 3 and 4, the knowledge level for
each node of the network in all the categories q and
the confidence level at time t have been reported, re-
spectively. The first value is calculated as the ratio of
A Dynamic and Context-aware Model of Knowledge Transfer and Learning using a Decision Making Perspective
69

Figure 2: Contexts correlation in the space.
the sum of the knowledge level held by node n
i
in all
the categories to the number of categories. Instead,
the second one is calculated as the ratio of the sum
of the confidence of all the relations of node n
i
to the
total number of nodes of set N. Each node is colored
according to the knowledge and confidence level held
at time t and the colors association is shown in Table
1 and in Table 2.
Figure 3: Starting Knowledge Level for the network nodes.
Table 1: Colors associated to the nodes depending on the
knowledge level held at time t.
Starting Knowledge Level (z) Color
0 ≤ z ≤ 0.2 Red
0.2 < z ≤ 0.4 Yellow
0.4 < z ≤ 0.6 Brown
0.6 < z ≤ 0.8 Blue
0.8 < z ≤ 1 Green
Figure 4: Starting Confidence Level for the network nodes.
Considering Equation 6, in order to track the dy-
namics of the knowledge transfer process, we take
into account 3 time instants, that are t + 5, t + 10
Table 2: Colors associated to the nodes depending on the
confidence level that it is associated for each node at time t.
Starting Confidence Level (s) Color
s ≤ 0.07 Light Blue
0.07 < s ≤ 0.08 Orange
0.08 < s ≤ 0.09 Grey
0.09 < s ≤ 0.1 Blue
s > 0.1 Pink
and t + 15. In Figure 5, each node is colored accord-
ing to the percentage of increased knowledge that it
holds after each t + T time instants, and, in particular,
the colors associated to each percentage interval are
shown in Table 3.
Table 3: Colors associated to the nodes depending on the
knowledge percentage held.
Knowledge Percentage (v
K
k
i
(t + T)) Color
v
K
k
i
(t + T) = 0 Red
0 < v
K
k
i
(t + T) ≤ 0.016 Yellow
0.016 < v
K
k
i
(t + T) ≤ 0.036 Brown
0.036 < v
K
k
i
(t + T) ≤ 0.06 Blue
0.06 < v
K
k
i
(t + T) ≤ 1
Green
As it is possible to see by observing Figure 5 and
considering different time instants, the level of knowl-
edge of each node changes dynamically. In particular
it increases, but due to the static nature of the net-
work, that is no nodes are added or removed, after a
certain time instant the process of knowledge transfer
will stop. What we would like to highlight is the pro-
gressive development of the knowledge level in the
network, due both to the risk aversion of each node,
through which the more it receives the more it is ad-
verse to assimilate, and the quality control of the re-
ceived knowledge introduced in this model. Consid-
ering Equation 7 and the time instants t + 5, t + 10
and t + 15, Figure 6 reports how dynamically the con-
fidence level changes over time. Each node is colored
according to its increasing or decreasing value of con-
fidence with respect to the other network nodes. The
colors are associated as shown in Table 4.
The reason of the dynamical behaviour of the
COMPLEXIS 2016 - 1st International Conference on Complex Information Systems
70

(a) Time t + 5 (b) Time t + 10 (c) Time t + 15
Figure 5: Dynamic of the knowledge transfer process for the Erd¨os-R´enyi model.
(a) Time t + 5 (b) Time t + 10 (c) Time t + 15
Figure 6: Dynamic of the confidence level for each node in the network following the Erd¨os-R´enyi model.
Table 4: Colors associated to the nodes depending on their
confidence values.
Confidence Value (c
K
k
i, j
(t + T)) Color
s = 0 Light Blue
s > 0 Orange
s < 0 Grey
increasing/decreasing confidence level is that, the
knowledge of the categories that they transferred in
a first period was not of a good quality, but after a cer-
tain time interval they start to transfer knowledge in
other categories whose quality is good, or viceversa.
As for the Erd¨os-R´enyi model, now we will show
how, using a Barab´asi-Albert model, the network
structure will affect the knowledge dynamics. In Fig-
ure 7 and 8 it is reported the knowledge and confi-
dence level for the network at time t and each node is
colored according to Tables 1 and 2.
Due to the fact that not all the nodes are con-
nected to each other and there are nodes with a very
few number of links, the starting confidence level is
really low, compared to the previous model, in fact
for all the nodes it is under the value of 0.07. At the
same time instants, the dynamics of the two network
models are different because the level of knowledge
increases slower than the previous case, as shown in
Figure 9. This is due to the structure of the network
itself. In fact, in this case the colors associated are dif-
Figure 7: Level of Knowledge for the network nodes.
Figure 8: Level of Confidence for the network nodes.
ferent, because in order to appreciate the knowledge
increasing we have to change the scale (The higher
increasing percentage is 0.00001%).
Similarly to what happens for the knowledge,
A Dynamic and Context-aware Model of Knowledge Transfer and Learning using a Decision Making Perspective
71

(a) Time t + 5 (b) Time t + 10 (c) Time t + 15
Figure 9: Dynamic of the knowledge transfer process for the Barab´asi-Albert model.
(a) Time t + 5 (b) Time t + 10 (c) Time t + 15
Figure 10: Dynamic of the confidence level for each node in the network following the Barab´asi-Albert model.
the mechanism of increasing/decreasing of the confi-
dence level is not so evident due to the high centrality
held by a little percentage of nodes.
From these results, it is observable that in a more
distributed network configuration the dynamics of
knowledge diffusion and of the confidence level are
observable much more than a centralized structure.
5 CONCLUSIONS AND FUTURE
WORKS
Nowadays, data, information and knowledge repre-
sent the core part of the network. The analysis of
their diffusion’s patterns could be helpful to predict
and study phenomena and node’s behaviour within
the network itself. Furthermore, by considering the
context as a variable that affects the network structure
and the knowledge held by the single node, adds fur-
ther complexity and dynamism to a process that al-
ready has these features. Compared to the previous
works, the main aim of the model presented in this pa-
per is to understand why a node, part of a network and
considered as a decision maker, decides whether to
accept or not knowledge from its neighboring nodes
that represent the set of alternatives. The decision is
based on the evaluation of each alternative based on
two decision criteria, the knowledge distance and the
confidence. In such a way, the structure of the net-
work and, in particular, the typology of the node’s
connections, both depending on the context, affect the
node’s decision. This process is also characterised by
a mechanism of confidence increasing and decreas-
ing, that occurs after the evaluation of the quality of
the knowledgereceivedat each time instant and which
adds dynamism to the model. In this sense, this work
is a first attempt to investigate how the introduction
of a context-aware decision making perspective in the
processes involving knowledge may vary its diffu-
sion’s pattern.
Future works will be focused on analysing the
process involving knowledge with the introduction of
other decision criteria, considering different contexts
and adding or removing links in the network. In such
a way different decision making scenarios and their
impact on the knowledge diffusion will be taken into
account.
ACKNOWLEDGEMENTS
This work has been funded by the “Programma
Operativo Nazionale” Ricerca & Competitivit´a
“2007-2013” within the projects “PON04a2
E
COMPLEXIS 2016 - 1st International Conference on Complex Information Systems
72

SINERGREEN-RES-NOVAE” and “PON04a2 C
Smart Health 2.0”
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