Computational Intelligence - August 2016 - 16
The knowledge graph represents huge amounts of
rich, heterogeneous and time-evolving information.
Querying the graph in a simple but efficient way is
then crucial for the usability of the system.
time nodes tc and tv with t c .label = t start and t v .label = t end ; v)
two edges e tmp
and e tmp
connecting oi to t c and t v .
ic
iv
Finally, we consider the concepts referred by the text in the
tweetsets and the most relevant users. Both concepts and users
are stored in the same way as described in Section V-A.
C. Enriching Social Media Information
with Knowledge from the EPG Content
The official EPG is often provided as a static content by broadcasters themselves. Although this makes us think about EPGs as nonsocial content, in our application this assumption is false. In fact,
EPGs may be enriched by information coming from the social
platform that provides users' rating, reviews, descriptions, and so
on. Including these social sources of information is required when
the official EPG content is poor.We consider a central social node
corresponding to the TV event, the unit of an EPG schedule. This
node is connected on one side to concepts identifying persons,
places and events referred by each TV event, on the other side to
concepts related to the TV event itself and its related TV program.
In detail, the portion of the knowledge graph related to a TV
event broadcasted from time tstart to time tend, is structured as follows: i) a social object node oi representing the TV event; ii) a representation edge e rep
ie connecting node oi to node ce, the conceptual
node corresponding to the TV event; iii) two time nodes tc and tv
with t c .label = t start and t v .label = t end ; iv) two edges e tmp
and
ic
e tmp
iv connecting oi to t c and t v .
Finally, we consider some concepts referred by EPG sources
in the following categories: people, places, events, genre and tv
channel. We refer to Section V-A for a detailed description of
how these concepts are stored.
D. Enriching Social Media Information
with Domain Ontologies
In addition to the social part of the knowledge graph, we consider other sources of knowledge that form the subgraph GO of
GK. In particular, we import ontology nodes from DBPedia2
(for general purpose concepts nodes) and a simplified version of
WordNet-Affect3 (for sentiment/emotion concept nodes).
Moreover, we enrich the EPG with conceptual nodes related to
the TV events. In particular, we link all TV event concept nodes
to a TV program node (for instance, we may link an hypothetical node concerning "Dexter, Season 4, Episode 12" to another
concept node related to "Dexter, Season 4" in its turn linked to
the more general concept of "Dexter (TV series)".
2
http://dbpedia.org/
http://wndomains.f bk.eu/wnaffect.html
3
16
IEEE Computational intelligence magazine | AUGUST 2016
VI. Querying the Knowledge Graph
The knowledge graph described in the previous section can potentially represent huge
amounts of rich, heterogeneous and timeevolving information. Accessing and querying
the graph in a simple but efficient way are
then crucial for the usability of the system.The
result-set of each query can be processed for
visualization and analysis purposes. To this end, we must define a
simpler model to represent the result-set of a query on the
Knowledge Graph G K . In particular, the result-set of a query is
modeled as an undirected weighted graph G Q = (V Q, E Q, W Q ),
where V Q is a set of vertices; E Q = {(v i, v j) s.t. v i, v j ! V Q} is a
set of undirected edges; W Q: V Q # V Q " R is the function that
associates a weight wij to each edge (v i, v j) ! E Q .
As an extension, the result-set may involve multiple query
graphs. Here, we consider a more general model consisting of a
collection of N query graphs G Q = {G Q1 , f, G QN }.
We can now define the general form of a graph query:
Definition 5 (graph query): Given a knowledge graph
G K , a query Q K (G K , P, F ) returns a collection of query
graphs G Q = F (G l ) , where: F is a mapping function
FGK : G K " G Q that associates vertices, edges and weights in
G K to vertices, edges and weights in G Q ; P is a selection predicate on G K , i.e., a function PGK : G K " {true, false}; G l 3 G K
is the subgraph of GK satisfying P.
This general definition embraces potentially any kind of
selection query. However, in our system, we focus on a specific
type of query called similarity query. The goal of this query is to
provide a graph where two vertices are connected if they are
similar enough. The weight of the edge connecting them measures the strength of their similarity. Before providing the definition of similarity query, we briefly introduce the definitions
of social context of a knowledge graph node. In the following,
the set of all social sources is denoted by KS .
Definition 6 (social context): Given a knowledge
graph G K = (V, E, W ) and a node v i ! V, a time interval
DT = (t start , t end ) and a set of social sources KS 3 KS, the
social context of v i in KS during DT is given by the undirectsc
ed graph G (vi, GK , KS, DT ) built on the subgraph of G K induced by
O
C
the nodes in V (vi, GK , KS, DT) ! O and V (vi, GK , KS, DT ) ! C where:
O
K
V (vi, G , KS, DT ) is the set of the nodes o j ! O such that
(i) 7 (o j, t c) ! E tmp s.t. t start # t c .label # t end , (ii) o j.source ! KS,
(iii) there is a path
Pij = ((v i, v i + 1), (v i + 1, v i + 2), f, (v i + n - 1, v i + n))
where v i + n = o j and 6k = 0fn, (v i + k, v i + k + 1) ! E str 0 (v i + k + 1, v i + k)
C
! E rep ; V (vi, GK , KS, DT ) is the set of nodes c k ! C such that
7 (o j, c k) ! E rep and such that all edges are undirected.
In a few terms, the social context of a node is the subgraph
induced by the social objects of a given social source and the
concepts associated directly to it in a given time interval. An
example of social context is given in Fig. 1, right side. The
notion of social context is central for the definition of similarity
Table of Contents for the Digital Edition of Computational Intelligence - August 2016
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