Semantic Relatedness with Variable Ontology Density

Rui Rodrigues

, Joaquim Filipe

and Ana L. N. Fred

Escola Superior de Tecnologia, Instituto Polit

ecnico de Set

ubal, Set

ubal, Portugal

Instituto de Telecomunicac¸

oes, Instituto Superior T

ecnico, Lisboa, Portugal

Keywords:

Semantic Relatedness, Wikipedia Categories, Ontological Entities, Taxonomical Density.

Abstract:

In a previous work, we proposed a semantic relatedness measure between scientiﬁc concepts, using Wikipedia

categories network as an ontology, based on the length of the category path. After observing substantial dif-

ferences in the arc density of the categories network, across the whole graph, it was concluded that these

irregularities in the ontology density may lead to substantial errors in the computation of the semantic re-

latedness measure. Now we attempt to correct for this bias and improve this measure by adding the notion

of ontology density and proposing a new semantic relatedness measure. The proposed measure computes a

weighed length of the category path between two concepts in the ontology graph, assigning a different weight

to each arc of the path, depending on the ontology density in its region. This procedure has been extended to

measure semantic relatedness between entities, an entity being deﬁned as a set of concepts.

1 INTRODUCTION

The semantic relatedness between two concepts indi-

cates the degree in which these concepts are related

in a conceptual network, by computing not only their

semantic similarity but actually any possible seman-

tic relationship between them (Ponzetto and Strube,

2007) (Gracia and Mena, 2008).

Computational semantic relationship techniques

can be placed into one of two categories:

• Distributional measures that rely on unstructured

data, such as large sets. The underlying assump-

tion is that if similar words appear in similar con-

texts, then they should have similar meanings.

• Measures based on structured databases, such as

taxonomies or ontologies, where semantic rela-

tionships are captured.

This work focuses on the second relationship mea-

suring category, and uses Wikipedia page categories

as a taxonomy.

The proposed measure considers not merely the

number of arcs in the graph between the nodes that

represent each concept, but also their relationship in

the taxonomy. In our work we used the English ver-

sion of Wikipedia

, based on which we analyzed re-

lationships between concepts by building paths from

start to destination nodes in the category network.

http://en.wikipedia.org

This procedure has been extended to measure seman-

tic relatedness between entities, an entity being de-

ﬁned as a set of properties, i.e. concepts.

Since the Wikipedia ontology is in constant de-

velopment, it was observed that some regions are far

more developed than others. In this paper we pro-

pose to add to our previously developed measure of

semantic relatedness (Medina et al., 2012) the no-

tion of density, as a function of the number of incom-

ing and outgoing links to/from a node in the concep-

tual graph. This means that the semantic relatedness

between concepts needs is inversely weighed by the

density of the region of the path between concepts.

We will propose a technique to compute the density

of this region.

The remaining sections of this document are or-

ganized as follows: in Section 2 we describe related

work in this area; Section 3 presents the proposed

measure of semantic relatedness with the inverse den-

sity ponderation and in section 4 are presented the re-

sults obtained after applying this measure to a set of

entities. Finally, in Section 5 we draw the main con-

clusions and identify opportunities for future work.

2 RELATED WORK

Given two words or expressions represented in a tax-

onomy, the computation of the semantic relatedness

554

Rodrigues R., Filipe J. and L. N. Fred A..

Semantic Relatedness with Variable Ontology Density.

DOI: 10.5220/0005189005540559

In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval (SSTM-2014), pages 554-559

ISBN: 978-989-758-048-2

 2014 SCITEPRESS (Science and Technology Publications, Lda.)

between these two objects may be transformed into

the evaluation of their conceptual distance in the con-

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

, 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-

http://en.wikipedia.org

based Measure (WLM), ﬁrst described in (Milne and

Witten, 2008), uses its hyperlink structure, rather than

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.

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 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.

The proposed relatedness measure is computed

from the following sequence of steps

Distance between Concepts - Weighted Edges

Sum. Let c

and c

be two concepts represented in

the Wikipedia categories network. Find the shortest

category path between the concepts. Compute the

edge-based semantic relatedness between c

and the

LCS node, which is the sum of the weights of the

edges that link c

to the LCS node. Repeat this proce-

dure to ﬁnd the edge-based semantic relatedness be-

tween c

and the LCS node.

The overall edge-based relatedness measure be-

tween the two concepts is given by

d(c

, c

) =

∑

i=0

∑

i=0

∑

i=0

∑

i=0

(1)

where w

is the weight of the edge with index i in

SemanticRelatednesswithVariableOntologyDensity

555

the category path between c

and the LCS category,

is the weight of the edge with index i in the cate-

gory path between c

and the LCS category, I is the

depth of the last edge of the path that connects c

the LCS and J is the depth of the last edge of the path

that connects c

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.

Edge-based Similarity between Entities. Given

two entities E

and E

represented by discrete sets of

concepts C

= {c

, ..., c

} and C

= {c

, ..., c

}, re-

spectively, we deﬁne the edge-based distance between

sets D(E

, E

) is

D(E

, E

) =

∑

i=1

∑

j=1

d(c

, c

)

n × m

(2)

Finally, we have the following similarity measure

between entities

S(E

, E

) = 1 − D(E

, E

) (3)

3.1 Computation of Shortest Paths in

the Wikipedia Graph

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 pro-

vided 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 determine if a page is of the ”Dis-

ambiguation 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 short-

est path computation. By regarding this shortest-path

Figure 1: Instatiation of the concept ”Machine Learning”.

search as a tree-search, each instance of the object cat-

egory will be a leaf of the tree.

1 C a t ego r y n e x t L e v e l ( C a t e g or y c )

2 Begin

3 Fo r E a c h C a t e g or y in c . List

4 Begin

5 If ( I t e r a t e d C a t e g o r y . List = null )

-> l e a f n o d e

6 Begin

7 Wi k i L i st = wiki p e d ia .

Ge t Ab o v e L e v e l C a t e g o r i e s

() ;

8 c. L i s t = newL i s t ;

9 End

10 Else

11 Begin

12 N e x t L ev e l ( I t e r a t ed C a t e g o r y );

-> re c u r si v e met h o d

13 End

14 End

15 End

Listing 1: Procedure to examine the upper level of a node.

After the instantiation of concept Machine Learn-

ing (see Figure 1), 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.

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).

1 Void Main ( )

2 Begin

3 List c 1 = w i k i pe d i a .

Ge t Ab o v e L e v e l C a t e g o r i e s (" c o n c ept 1 ")

;

4 List c 2 = w i k i pe d i a .

Ge t Ab o v e L e v e l C a t e g o r i e s (" c o n c ept 2

") ;

6 While ( E xi s t M a t c h (c1 , c2 ) )

7 Begin

8 ne x t L e ve l ( c 1 ) ;

9 ne x t L e ve l ( c 2 ) ;

10 End

11 End

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”.

KDIR2014-InternationalConferenceonKnowledgeDiscoveryandInformationRetrieval

556

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

but it was sufﬁciently effective to

accomplish our goals.

3.2 Density

The general idea behind the proposed improvement

to the measure can be introduced with some generic

mathematics concepts. In graph theory, a dense graph

is a graph in which the number of edges is close to the

maximal possible between all the vertices contained.

The maximum density is 1 and the minimum is 0

for a very sparse graph (Coleman and Mor

e, 1983).

In our case, we do not intend to calculate the com-

plete density of the Wikipedia category ontology. In-

stead, we propose to analyse all the nodes in the short-

est path and ﬁnd all the degrees. Then it is necessary

to ﬁnd a way of discover the approximate level of den-

sity with the degrees as a clue.

Figure 2: Example of an High Density path.

In the ﬁgure above, the larger circles represents

Wikipedia categories in the path, the small ones rep-

resent the neighbors and the squares represents the

Wikipedia pages. As it can be observed, Figure 2 rep-

resents a situation of higher density than Figure 3 and

as we can conclude, the number of degrees for each

node follows the tendency.

When path calculation occurs, the degrees of all

vertices in the calculated path are saved, even the

number of categories at the original pages.The objec-

tive is to save as much information as possible about

the number neighbors of the calculated path. As it

was previous explained, the properties of our entities

were mapped to the correct page that represents the

See http://bugs.sun.com/view bug.do?bug id=4726340

Figure 3: Example of a Low Density Path.

concept in Wikipedia. Figure 2 aims to ﬁnd the dis-

tance between the property generic algorithm and the

property boosting. These two concepts are contained

in two different sets of properties, each one represent-

ing it own entity.

For every path explored between the entity prop-

erties the average number of neighbors is calculated.

This means that information about the average num-

ber of nodes that are between the two entities is kept

across the paths calculations, and based on that, al-

lows to understand if the universe that we are working

at the moment is more or less dense.

PathDensity(p1, p2) =

∑

| E | (i)

(4)

PropertyDensity =

PathDensity(p1, p2)

PathDensity(p1, p2)+RootDensity(lcs,root)

(5)

On top of that, as we saw before, the LCS node is

very important to us. In a previous work it was con-

cluded that it is very helpful to calculate how deep is

this node (Medina et al., 2012). Now we propose to

use additional information, by taking into account the

density of the path between a given LCS and the root.

The strategy is similar to the density calculation prop-

erty, with the difference that now our path is between

our LCS and the Wikipedia root category. We count

the neighbors at each vertex and calculate the average

at the end.

4 RESULTS AND DISCUSSION

With test result tracking in mind, we decided to use

the same battery of entity tests from last paper (Med-

ina et al., 2012). The results can be consulted on table

SemanticRelatednesswithVariableOntologyDensity

557

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.

Pair (E

, E

) (E

, E

) (E

, E

) (E

, E

) (E

, E

) (E

, E

) (E

, E

) (E

, E

) (E

, E

)

S Eq.3 0.948 0.931 0.912 0.869 0.954 0.826 0.786 0.862 0.989

S Den 1.0 0.958 0.682 0.812 1.0 0.724 0.847 0.826 1.0

2, where the ﬁrst row contains the values previously

obtained on our ﬁrst paper, and the second row con-

tains the results achieved by the new method. The

set is composed by six entities: three of them rep-

resent well known conferences (CVPR

, KDD

and

RECOMB

). These conferences were chosen because

each one corresponds to a distinct scientiﬁc research

area: CVPR to Computer Vision and Pattern Recog-

nition; KDD to Data Mining and Knowledge Discov-

ery; RECOMB to Computational 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 conferences:

• Author E

: from computer vision area, which

matches CVPR represented by E

• Author E

: from data mining and machine learn-

ing areas, which are more related to the KDD, rep-

resented by E

• Author E

: from genetics and bioinformatics ar-

eas, which is more closely related to RECOMB

(represented by E

Some concepts listed here do not have a direct cor-

respondence with a Wikipedia page, so it lead us to a

disambiguation problem. A quick solution for the ﬁrst

case was to replace the concept with a similar con-

cept. For instance, the concept Image Segmentation

of E1 had to be replaced with the page Segmentation

(image processing). We will propose a technique to

automation this kind of tasks in a future work.

From these results, as before, we observed a high

value of similarity for the following entity pairs:

http://www.cvpr2012.org/

http://kdd2012.sigkdd.org/

http://bioinfo.au.tsinghua.edu.cn/recomb2013/

, E

), (E

, E

), and (E

, E

), but at this time, there

is even stronger. This is expected due to the semantic

overlapping of properties in the sets. It was also ex-

pected that the similarity values for (E

, E

) would be

much lower than the value found for (E

, E

). It was

observed that in many cases this distance continues

to return very high similarity values that are not quite

differentiated. We believe this is due to the proxim-

ity of a single pair of features, among many features.

The fact that similarity between entities is determined

by the maximum value of the similarity between all

combinations of pairs of entities features introduces

”bias” to 1. Hence it is needed, eventually, in the fu-

ture to introduce mechanisms to extend the dynamic

range of the similarity between entities. However, the

high similarity E1-E5 is explained due to some very

similar features, such as Pattern Recognition vs. Ma-

chine Learning, for example.

5 CONCLUSIONS AND FUTURE

WORK

In this paper we continue our journey to ﬁnd a more

balanced semantic relatedness measure between enti-

ties. As before, we belive that the use of Wikipedia

and the hierarchy of scientiﬁc categories contained in

it, is the most promising way to accomplish your goal.

The devised measure examines the Wikipedia cat-

egory paths between all the possible concept pairs of

two distinct entities, assigning weights according to

the category’s relevance. With this new attempt, we

improve this measure by adding the notion of ontol-

ogy density. We examined and compared new results

with the old ones and concluded, by observing, that

these matchs are a step in the right direction. Altough

KDIR2014-InternationalConferenceonKnowledgeDiscoveryandInformationRetrieval

558

there is room for future developments, mainly regard-

ing the range of result values, the differentiation of

values and eventually the introdution of a threshold.

The issue is to determine where to place the threshold

to make the right decision. Usually the threshold is

set ”half-way”, however, for this test case, it should

be placed above 0.73, which is a high value.

Future work includes continuing exploration of

the measure for other contexts as well as a compari-

son of our measure with other state-of-the-art metrics.

It is also very important the effort to make the process

as much autonomous as possible, by giving to the pro-

cess the ability of automatic disambiguation.

ACKNOWLEDGEMENTS

The authors wish to acknowledge the support

of the Escola Superior de Tecnologia, Instituto

Polit

ecnico de Set

ubal (EST-IPS) and Instituto de

Telecomunicac¸

oes (IT-IST).

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