Computational Intelligence - August 2016 - 14

Concept
Social Object
Subject Node
Time Node
Representation
Structure
Support

C5
C6

C4

O2

O1

T1
O4
C1
C3

O6

O4
O5

C9

C1

T2
C8

C6

C4

O2

O1

T1

C7

O3
C2

T3

C7

O3
C2

T2

C10

Figure 1 An example of a knowledge graph representation with all types of nodes and edges (left) and the social context of node C1 (right).

We capture different existing relationships between subjects
and social objects, and between social objects and concepts: a
group of subjects that recognize a social value of an act supports
the resulting social object (e.g. the contractors support the contract); a social object represents a social instance of some concepts
on a precise context (e.g. a video may represent a volleyball
match). Other relationships exist among entities of the same type.
We call these relationships structural dependencies. A social object o1
is structurally dependent on another object o2 if o1 is a part of o2 (e.g.
a comment is a part of a video). A subject can be structurally
dependent on a group of subjects (e.g. a subscriber is a part of
playlist subscribers) that performed the same kind of actions on
the same social object. A concept may be structurally dependent on
a more general concept (e.g. hilarity is a specialization of joy).
Finally, we capture the fact that social objects evolve with
time. Hence, as a special case of representation relationship, we
consider the temporal relation between a social object and a temporal concept (e.g. a video has been posted in a specific time
instant, and has been viewed during a specific time period).
Based on the above, in the following subsection, we formally define the knowledge graph.

E rep = {(o i, c j) s.t. o i ! O, c j ! C} is the set of representation edges,
and E str = {(v i, v j) s.t. v i, v j ! S 0 v i, v j ! O 0 v i, v j ! C, i ! j} is
the set of structural dependency edges. The edge weighting function
is W: E " (0, 1] .
Each node v ! V has three attributes: v.label, v.subtype and
v.magnitude, representing the name of the concept associated to
the node, an application-specific type and the number of
instances of such concept recognized in the data sources, respectively. Optionally, each edge e ! E may be characterized by an
attribute e.subtype which specifies an application-specific type.
Moreover, given the set of time concepts T 3 C, E tmp 3 E rep
denotes the set of edges (oi, tj), where o i ! O, and t j ! T.
A special subgraph of GK is the ontology graph.Vertices in the
knowledge base are all concepts belonging to C, defined as follows.
Definition 2 (Ontology Graph): The ontology graph
G O (V O, E O, W O ) is the subgraph of GK induced by V O = C.
Thus, E O is a set of structural dependency edges encoding several ontology relationships (such as: "is a", "part of "), possibly
specified by the attribute e.subtype.
Fig. 1 (left side) shows a small example of knowledge graph
in which every type of node and edge is represented.

A. Knowledge Graph

V. Extracting Information from
Social Media Data Sources

The knowledge graph (first introduced in [27]) models all the
relationships between social objects, subjects and concepts
introduced so far.
Definition 1 (Knowledge Graph): Let O, S and C be the sets of
all social objects, subjects and concepts, respectively. Let T 3 C
be the set of temporal concepts. The cross-network knowledge
graph on O, S, and C, is the directed weighted graph
G K (V, E, W), where the set of vertices is V = O , S , C, the
set of edges is E = E sup , E rep , E str including edges representing support relationships, representation relationships as
well as structural dependency relationships. In particular
E sup = {(s i, o j) s.t. s i ! S, o j ! O} is the set of support edges;

14

IEEE Computational intelligence magazine | AUGUST 2016

In this section, we describe how the user-generated content
publicly available on social media is processed and mapped to
the concepts and relationships to be represented in the knowledge graph defined in Section IV.
A. Facebook Content

Facebook is the most famous and widespread social networking
platform. Since it has about 1.5 billions of monthly active users,
TV companies use it for stimulating discussions around TV
shows. Through the website, users may post, watch, comment,
like or dislike any kind of multimedia comments: text comments,



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