Measuring Entity Semantic Relatedness using Wikipedia
Liliana Medina
1
, Ana L. N. Fred
2
, Rui Rodrigues
3
and Joaquim Filipe
3
1
INSTICC, Set
´
ubal, Portugal
2
Instituto de Telecomunicac¸
˜
oes, Instituto Superior T
´
ecnico, Lisboa, Portugal
3
Escola Superior de Tecnologia, Instituto Polit
´
ecnico de Set
´
ubal, Set
´
ubal, Portugal
Keywords:
Semantic Relatedness, Wikipedia, Ontological Entities.
Abstract:
In this paper we propose a semantic relatedness measure between scientific concepts, using Wikipedia as an
hierarchical taxonomy. The devised measure examines the length of Wikipedia category path between two
concepts, assigning a weight to each category that corresponds to its depth in the hierarchy. This procedure
was extended to measure the relatedness between two distinct concept sets (herein referred to as entities),
where the amount of shared nodes in the paths computed for all possible concept sets is also integrated in a
global relatedness measure index.
1 INTRODUCTION
The semantic relatedness of two concepts indicates
how far apart these concepts are when represented
in a conceptual network or taxonomy, by using in
the computation of this distance value all the seman-
tic relationships that exist between them(Ponzetto and
Strube, 2007). These semantic relationships may be
hierarchical (IS-A type, hypernymy, hyponomy), of
equivalence (synonyms) or associative (cause/effect)
(Jiang and Conrath, 1997).
Semantic relatedness computation techniques
may be grouped into two categories:
1. Distributive measures, that rely on non-structured
data, such as large corpora. The underlying hy-
pothesis is that similar words appear in similar
contexts, thus they must have similar meanings.
These approaches capture relationships between
words.
2. Measures based on structured databases, such as
taxonomies or ontologies, where relationships be-
tween semantic concepts are captured.
This paper focuses on the second category of re-
latedness measures, using Wikipedia as a taxonomy.
Our proposed measure takes into account both the
distance between concepts and the relationship of the
concepts with others in the taxonomy. It is then gen-
eralized to measure the relatedness between concept
sets or entities. As defined in (Rodrguez and Egen-
hofer, 2003), the term entity refers to groups of con-
cepts or objects in the real world that are somehow
semantically related.
Our goal is then to present a cost function that al-
lows us to make a decision on similarity between two
or more entities, based on their representations as con-
cept sets.
As an ontological basis, we will use the english
version of Wikipedia
1
to help establish semantic re-
lationships between concepts and entities. In the re-
cent years, Wikipedia has been explored as a potential
knowledge base for a number of information retrieval
tasks, such as text categorization, named entity recog-
nition and semantic relatedness computation(Zesch
et al., 2008).
The remaining sections of this paper are organized
as follows: in Section 2 we describe related work in
this area; Section 3 presents the proposed similarity
measure, Section 4 deals with the results obtained af-
ter applying this measure to a set of entities. Finally,
in Section 5 we draw the main conclusions and pro-
posals of future work.
2 RELATED WORK
Given two words or expressions represented in a tax-
onomy, the computation of the semantic relatedness
between these two objects may be transformed into
the evaluation of their conceptual distance in the con-
1
http://en.wikipedia.org
431
Medina L., L. N. Fred A., Rodrigues R. and Filipe J..
Measuring Entity Semantic Relatedness using Wikipedia.
DOI: 10.5220/0004180204310437
In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval (SSTM-2012), pages 431-437
ISBN: 978-989-8565-29-7
Copyright
c
 2012 SCITEPRESS (Science and Technology Publications, Lda.)

ceptual space generated by a taxonomy (Jiang and
Conrath, 1997), being that each object is represented
by a node in the resulting graph.
Semantic relatedness measures in hierarchical tax-
onomies can be categorized into three types (Slimani
et al., 2006):
1. Information Content or Node-based: evalua-
tion of the information content of a concept rep-
resented by a node such as described in (Resnik,
1999). The semantic relatedness between two
concepts reflects the amount of shared informa-
tion between them, generally in the form of their
least common subsumer (LCS).
2. Path or Edge-based: evaluation of the distance
that separates concepts by measuring the length
of the edge-path between them (Wu and Palmer,
1994) (Rada et al., 1989). A weight is assigned
to each edge, being that the weight computation
must reflect some of the graph properties (network
density, node depth, link strength, etc.) (Jiang and
Conrath, 1997)
3. Hybrid: a combination of the former two (Jiang
and Conrath, 1997) (Leacock and Chodorow,
1998).
Lexical databases, such as WordNet, have been
explored as knowledge bases to measure the semantic
similarity between words or expressions. However,
WordNet provides generic definitions and a somewhat
rigid categorization that does not reflect the intuitive
semantic meaning that a human might assign to a con-
cept.
In this paper we use the english version of the
Wikipedia
2
, a web-based encyclopedia which has ap-
proximately 4 million articles edited and reviewed by
volunteers. The contributors are asked to assign these
articles to one or more categories: Wikipedia may
be thus viewed as either a folksonomy (Nastase and
Strube, 2008) or a Collective Knowledge Base (Zesch
et al., 2008), where human knowledge and human in-
tuition on semantic relationships emerges in the form
of a category network. It is then natural that this web-
resource has been increasingly explored as a concep-
tual feature space, such that articles and categories are
represented as nodes in the Wikipedia graph.
Techniques such Explicit Semantic Analysis
(ESA) (Gabrilovich and Markovitch, 2007) repre-
sent texts in the high-dimensional concept space of
Wikipedia as weighted vectors. A textual fragment
is thus considered as a weighted mixture of a prede-
termined set of ”natural” concepts. Wikipedia Link-
based Measure (WLM), first described in (Milne and
Witten, 2008), uses its hyperlink structure, rather than
2
http://en.wikipedia.org
the category hierarchy or textual content to compute
semantic relatedness. In (Gouws et al., 2010) seman-
tic relatedness is computed by spreading activation
energy over the aforementioned hyperlink structure.
Our proposed measure takes into account not only
the connections between nodes, but also the nature of
nodes themselves by means of their location and con-
nectivity degree in the overall category network. The
measure takes into account only the categories and
subcategories which encompass a given pair of con-
cepts, discarding the actual textual content of the con-
cepts article pages in the Wikipedia. We also general-
ize this approach to measure the semantic relatedness
between sets of concepts.
Measurement of semantic similarity between con-
cept sets can provide particular value for tasks con-
cerning the semantics of entities (Liu and Birnbaum,
2007). An entity may represent, for instance, (1) an
author, by means of his/her research interests, (2) a
publication, such as a scientific journal, by means of
its main topics, (3) a conference, by means of its sub-
mission topics. In Information Retrieval, the simi-
larity between documents is generally estimated by
means of their Vector Space Models. Each feature
vector represents the bag-of-words of the respective
document, assigning a weight to each feature/term
that reflects its importance in the overall context of
either the document or the document set. The def-
inition of entity can also be extended to represent a
document, where instead of a weighted feature vec-
tor, we have a set of terms that can be related to other
entities (which may also be documents or other types
of entities) by means of a semantic relatedness mea-
sure between entities, such as the one presented in this
paper.
3 PROPOSED SIMILARITY
RELATEDNESS MEASURE
The implementation of the proposed measure is based
on the assumption that each pair of concepts is con-
nected by a category path, such as the one depicted in
Figure 1 for the pair of concepts ”Feature Learning”
and ”Boosting”. In this Figure we may observe that
the least common subsumer (LCS) of both concepts
is ”Machine Learning”.
The proposed relatedness measure is computed
from the following sequence of steps
Distance between Concepts - Weighted Edges
Sum. Let c
1
and c
2
be two concepts represented in
the Wikipedia categories network. Find the shortest
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Root
Category
Boosting
Classification
Algorithms
Ensemble
Learning
Machine
Learning
Feature
Learning
...
i=0
j=2
j=1
j=0
r=0
r=R
Figure 1: Category path between concepts Feature Learn-
ing and Boosting.
category path between the concepts. Compute the
edge-based semantic relatedness between c
1
and the
LCS node, which is the sum of the weights of the
edges that link c
1
to the LCS node. Repeat this proce-
dure to find the edge-based semantic relatedness be-
tween c
2
and the LCS node.
The overall edge-based relatedness measure be-
tween the two concepts is given by
d(c
1
, c
2
) =
∑
I
i=0
w
1
i
+
∑
J
i=0
w
2
i
∑
R
i=0
w
1
i
+
∑
R
i=0
w
2
i
(1)
where w
1
i
is the weight of the edge with index i in
the category path between c
1
and the LCS category,
w
2
i
is the weight of the edge with index i in the cate-
gory path between c
2
and the LCS category, I is the
depth of the last edge of the path that connects c
1
to
the LCS and J is the depth of the last edge of the path
that connects c
2
to the LCS category, with R denoting
the index of the last edge in the path between the root
node and a concept node, with the restriction that this
path must include the LCS.
The exponential weight of the i −th edge is given
by
w
i
= β
α
˙
i
(2)
where α and β are predefined parameters.
Edge-based Similarity between Entities. Given
two entities E
1
and E
2
represented by discrete sets of
concepts C
1
= {c
1
1
, ..., c
1
n
} and C
2
= {c
2
1
, ..., c
2
m
}, re-
spectively, we define the edge-based distance between
sets D(E
1
, E
2
) is
D(E
1
, E
2
) =
∑
n
i=1
∑
m
j=1
d(c
i
, c
j
)
n × m
(3)
Finally, we have the following similarity measure
between entities
S(E
1
, E
2
) = 1 − D(E
1
, E
2
) (4)
Weighted Shared Nearest Neighbor Nodes Simi-
larity between Entities (SNN). Let c
1
and c
2
be
two concepts such that c
1
∈ E
1
and c
2
∈ E
2
. Compute
all the shortest paths between c
1
and the concepts be-
longing to E
2
and follow the same procedure for all
possible pairs of c
2
with concepts that belong to E
1
.
We compute the shortest paths between c
1
and all
the categories belonging to E
2
, and define C
1
to be the
set of categories found in these paths. Conversely, we
determine paths from c
2
to the entity E
1
and define C
2
in a similar way.
We refer to C
1
and C
2
as nearest-neighbor sets of
c
1
and c
2
, respectively. The shared nearest-neighbors
of c
1
and c
2
correspond to their intersection, C
1
∩C
2
.
We define then the following weight, proportional to
the amount of shared neighbors
SNN(c
1
, c
2
) = 2
|C
1
∩C
2
|
|C
1
| + |C
2
|
(5)
which is generalized to measure the weight of
shared categories between two entities
SNN(E
1
, E
2
) = 2
n
∑
i∈E1
m
∑
j∈E2
|C
i
∩C
j
|
|C
i
| + |C
j
|
(6)
The location of a node in the category network
may influence its relevance in the overall computa-
tion of the relatedness measure between the entities.
A node located deeper in the hierarchy is more spe-
cific, and therefore more relevant to the characteriza-
tion of the semantic proximity of two concepts. If a
category path contains only a few categories, located
at deeper levels of the hierarchy, then the concepts
encompassed by these categories are closer together
than concepts encompassed by categories further up
in the hierarchy.
By assigning weights to the nodes, Equation 6 be-
comes
W
SNN
(E
1
, E
2
) = 2
∑
l∈C
i
∩C
j
w
n
(l)
∑
l∈C
i
w
n
(l) +
∑
l∈C
j
w
n
(lcs)
(7)
where w
nl
is the depth of the l node (equal to the
number of edges between the current node and the
root node).
Proposed Relatedness Measure. A proposed mea-
sure results from the combination of the former
weighted similarity measures.
M(E
1
, E
2
) =
α
1
S(E
1
, E
2
) + α
2
W
SNN
(E
1
, E
2
)
α
1
+ α
2
(8)
where α
1
and α
2
are predefined parameters.
MeasuringEntitySemanticRelatednessusingWikipedia
433

This measure may be further enhanced by the
Weighted Shared Least Common Subsumer Nodes
(S
LCS
). This weighting reflects the assumption that,
if two entities share a large amount of least com-
mon subsumers, and if these nodes are located further
down in the category hierarchy, than the entities must
be strongly related. On the other hand, if the common
subsumers are few, or located in the upper levels of
the hierarchy than the semantic relatedness between
the entities must be weak.
The following weight is assigned to the shared
LCS nodes of two entities,
S
LCS
(E
1
, E
2
) =
∑
lcs∈{C
1
∩C
2
}
w
n
(lcs)
∑
lcs∈C
1
w
n
(lcs) +
∑
l∈C
2
w
n
(lcs)
(9)
where the weight of the node, w
node
(l) corre-
sponds to its depth in the hierarchy.
Hence, Equation 8 takes the following form
M(E
1
, E
2
) =
α
1
S(E
1
, E
2
) + α
2
S
LCS
(E
1
, E
2
) + α
3
SNN(E
1
, E
2
)
α
1
+ α
2
+ α
3
(10)
where α
1
, α
2
and α
3
are predefined parameters.
3.1 Computation of Shortest Paths in
the Wikipedia Graph
Figure 3: An object of the type CATEGORY.
Figure 4: Instatiation of the concept ”Machine Learning”.
Several versions of the Wikipedia maybe be
accessed at http://dumps.wikimedia.org/backup-
index.html. For the results presented in this
paper, we used a recent english version. To store
all the Wikipedia pages and links we used the
MySQL structure provided by the Java Wikipedia
Library API (available at http://www.ukp.tu-
darmstadt.de/software/jwpl/), further described in
(Zesch et al., 2008). This API also helps us to deter-
mine if a page is of the ”Disambiguation pages” type.
We did not, however, used the API to build category
paths, having specifically devised procedures for this
task.
Each Wikipedia object of the type CATEGORY is
assigned a level within the current search and a list
of its nearest neighbors, when examined for shortest
path computation. A representation of these CATE-
GORY objects is depicted in Figure 3 By regarding this
shortest-path search as a tree-search, each instance of
the object category will be a leaf of the tree.
1 Ca t e g o ry ne xtL e v e l ( C ate g o r y c )
2 Begin
3 For E a c h Cat e g o ry in c . L i s t
4 Begin
5 If ( I t e ra t e d C a te g o r y . L i s t = nul l )
-> l e a f node
6 Begin
7 Wi k i L i st = wiki p e d ia .
Ge t Ab o ve L e v e l C a te g or i e s
() ;
8 c. L i s t = newLi s t ;
9 End
10 Else
11 Begin
12 Ne x t L ev e l ( I t e r a t ed C a t e g or y );
-> re c u r siv e me thod
13 En d
14 End
15 En d
Listing 1: Procedure to examine the upper level of a
node.
After the instantiation of concept Machine Learn-
ing (see Figure 4), all of its parent categories (”Ar-
tificial Intelligence”, ”Learning” and ”Computational
Statistics”) will have a level attribute of two. The in-
statiation of each of these categories will return their
corresponding list of parent categories and a level at-
tribute of 3 and so on. The pseudo code in Listing 1
illustrates this procedure.
The GETABOVELEVELCATEGORIES() method
searches the parent categories of the current category,
C. For each computation of a category path between
two concepts, two trees are built, one for each con-
cept. The level attribute will grow until the algorithm
finds a common ancestor (the LCS).
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Concept i
Concept 1
Concept 3
Concept 2
...
ENTITY 1
ENTITY 2
Concept j
...
ENTITY 2
ENTITY 1
...
...
...
Concept 1
Concept 2
Concept 3
LCS
LCS
LCS
LCS
LCS
LCS
Ci - Nearest neighbor nodes of concept i with respect to Entity 2
...
Cj - Nearest neighbor nodes of concept j with respect to Entity 1
Ci
Cj
LCS
Root
Wn = depth in the hierarchy
Figure 2: Illustration of the various components of the proposed semantic relatedness measure.
1 Vo i d Ma i n ()
2 Begin
3 Lis t c 1 = w i ki p e d ia .
Ge t Ab o ve L e v e l C a te g or i e s (" co n c e p t1 ")
;
4 Lis t c 2 = w i ki p e d ia .
Ge t Ab o ve L e v e l C a te g or i es (" co n c e p t2
") ;
5
6 While ( E xi s t M at c h (c1 , c2 ) )
7 Begin
8 nex t L e ve l ( c1 ) ;
9 nex t L e ve l ( c2 ) ;
10 En d
11 En d
Listing 2: Procedure to find a path by means of a least
common subsumer search.
The pseudo code in Listing 2 illustrates this pro-
cedure for example concepts ”concept1” and ”con-
cept2”.
The method EXISTMATCH examines all the nodes
of the two trees: if a match is found, then this node
(which is the Least Common Subsummer) is returned.
The shortest category path between the concepts is
then found by examining the categories between the
concepts and the LCS. To reduce the computational
costs, each search is stored in a cache table.
These procedures were implemented with Java.
Java is not the most adequate technology for this type
of tree search, since it lacks tail call elimination for se-
curity reasons, as further detailed in the Bugs section
of Oracles website
3
but it was sufficiently effective to
accomplish our goals.
4 RESULTS AND DISCUSSION
For testing the proposed measure, we have chosen 6
entities: three of them represent well known confer-
ences (CVPR
4
, KDD
5
and RECOMB
6
). These con-
ferences were chosen because each corresponds to a
3
See http://bugs.sun.com/view bug.do?bug id=4726340
4
http://www.cvpr2012.org/
5
http://kdd2012.sigkdd.org/
6
http://bioinfo.au.tsinghua.edu.cn/recomb2013/
MeasuringEntitySemanticRelatednessusingWikipedia
435

Table 1: Entities represented as sets of scientific topics. The first three entities represent actual conferences (CVPR, KDD and
RECOMB respectively). The other three entities represent authors.
E1 E2 E3 E4 E5 E6
Computer Vision Knowledge Discovery Molecular Biology Computer Vision Information Extraction Genetics
Object Recognition Data Mining Gene Expression Robotics Machine Learning Human Genome
Structure from Motion Web Mining Computational Biology Object Recognition Natural Language Processing Gene Expression
Image Segmentation Recommender Systems Genomics Structure from Motion Information Retrieval Systems Biology
Image Processing Cluster Analysis Population Genetics Human Computer Interaction Data Mining Clinical Medicine
Object categorization Text Mining Virtual Reality Graphical Model Bioinformatics
Optical Flow Data Analytics Facial Expression Social Network
Pattern Recognition Structure Mining Reinforcement Learning
Web Mining
Table 2: Proposed similarity measure results. The chosen parameter values are α = 1 and β = 3.
Pair (E
1
, E
4
) (E
1
, E
5
) (E
1
, E
6
) (E
2
, E
4
) (E
2
, E
5
) (E
2
, E
6
) (E
3
, E
4
) (E
3
, E
5
) (E
3
, E
6
)
S Eq.4 0.948 0.931 0.912 0.869 0.954 0.826 0.786 0.862 0.989
W
SNN
Eq. 7 0.721 0.655 0.522 0.672 0.599 0.542 0.538 0.432 0.681
S
LCS
Eq. 9 0.356 0.251 0.209 0.265 0.331 0.261 0.194 0.206 0.331
M Eq. 10 0.675 0.612 0.547 0.602 0.628 0.543 0.506 0.500 0.667
distinct scientific research area: CVPR to Computer
Vision and Pattern Recognition; KDD to Data Min-
ing and Knowledge Discovery; RECOMB to Compu-
tational Molecular Biology. The other three entities
represent well known authors. Each of these authors
was chosen based on the strong correspondence of
their research interests with one of the three confer-
ences:
• Author E
4
: research interests in the area of com-
puter vision and object recognition (from images),
which matches CVPR represented by E
1
.
• Author E
5
: research interests in the area of data
mining and machine learning, which are more re-
lated to the scientific areas of KDD, represented
by E
2
, but can also be related to CVPR by means
of the topic ”Pattern Recognition”.
• Author E
6
: research interests in the area of ge-
netics and bioinformatics, which is more closely
related to RECOMB (represented by E
3
) than the
other two conferences, although bioinformatics
also overlaps the areas of KDD.
The topic set that represents each entity was de-
rived from the conference or authors official website.
Some scientific concepts do not exist in the
Wikipedia version or lead to disambiguation pages. A
solution for the first case was to replace the concept
with a similar one that does exist in the Wikipedia.
For instance, the concept ”Image Segmentation” of
E
1
had to be replaced with the page ”Segmentation
(image processing)”. A possible solution for the sec-
ond case would be to access the disambiguation page,
retrieve the Wikipedia links there listed and choose
from these the most appropriate one for our search by
examining their nearest neighbor categories. This al-
ternative will be explored in future work.
The chosen parameter values are α = 1, β = 3,
α
1
= 1, α
2
= 1 and α
3
= 1. The depth of each node
in the Wikipedia hierarchy is determined with respect
to the category entitled ”Main topic classifications”
7
which encompasses Wikipedia’s major categories.
The concept sets that represent the entities are de-
picted in Table 1. The values obtained with the pro-
posed measure are in Table 2, were the different com-
ponents are depicted in separate rows. The overall re-
latedness measure values correspond to the last row.
From these values we observe a high value of
similarity relatedness for the following entity pairs:
(E
4
, E
1
), (E
5
, E
2
), and (E
6
, E
3
). This is expected
due to the semantic overlapping of these entities.
It was also expected that the similarity values for
(E
1
, E
5
) would be much lower than the value found
for (E
1
, E
4
). The underlying cause may be the ”Pat-
tern Recognition” concept of E
1
which is strongly
correlated with the concepts of E
5
. Other possible
cause may be the S component of the proposed mea-
sure (see Equation 4): it relies strongly on the com-
putation of the distance from the nodes in the cate-
gory paths to the root node which was chosen to be
”Main topic classifications”. We observe that in many
cases this distance is very high, which originates high
similarity values that are not quite differentiated from
entity to entity. This also has some impact in the
other components of the measure. A solution for this
7
http://en.wikipedia.org/wiki/Category:Main topic class
ifications
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would be to choose as root node a category lower in
the Wikipedia hierarchy than ”Main Topic Classifi-
cations”, possibly a node that still encompasses the
overall topics of the entity, but not as generic as the
one chosen here. The values of the parameters are
being fine-tuned in ongoing work in order to further
improve the proposed measure.
5 CONCLUSIONS
In this paper we presented a new semantic related-
ness measure between entities, using Wikipedia as a
hierarchy of scientific categories. The devised mea-
sure examines the Wikipedia category paths between
all the possible concept pairs of two distinct entities,
assigning weights according to the category’s rele-
vance in the resulting path set and in the Wikipedia
graph. We examined the proposed measure values
for selected entities, observing that these match the
intuitive human assessment of their semantic similar-
ity. We conclude then that this is a valid approach
to automatically assess the proximity of scientific re-
searchers and other scientific entities such as confer-
ences and journals.
Future Work. Ongoing work includes com-
parison of the results obtained with man-
ual annotations done by volunteers, using a
website specifically deployed for this task
(www.insticc.org/SemanticDistance.aspx). Fur-
ther work includes continuing exploration of the
measure for other entity pairs, comparison of our
measure with other state-of-the-art metrics, devising
tasks of semantic disambiguation of Wikipedia arti-
cles and clustering of concept sets such that an entity
may be represented by several subsets of scientific
topics, each subset representing a particular area.
ACKNOWLEDGEMENTS
The authors wish to acknowledge the support of
the Instituto de Telecomunicac¸
˜
oes (IT-IST) and Es-
cola Superior de Tecnologia, Instituto Polit
´
ecnico de
Set
´
ubal (EST-IPS).
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