Identifying global representative classes of
DBpedia Ontology through multilingualanalysis: A rank aggregation approach
Eun-kyung Kim and Key-Sun Choi
School of Computing, Korea Advanced Institute of Science and Technology (KAIST),
Daejeon, Republic of Korea{kekeeo,kschoi}@world.kaist.ac.kr

Abstract. Identifying the global representative parts from the multilingual pivotal ontology is important for integrating local language resources into Linked Data. We present a novel method of identifying global
representative classes of DBpedia ontology based on the collective popularity, calculated by the aggregation of ranking orders from Wikipedia’s
local language editions.
Keywords: DBpedia, Multilingual, Ontology, Rank Aggregation

1Introduction

The diversity and amount of data on the Web are both continuously growing,
and there has been a paradigm shift leading from the publishing of isolated data
to the publishing of interlinked data through a variety of knowledge sources suchas Linked Open Data (LOD) [1]. DBpedia dataset [2] currently plays a central
role in the LOD cloud, which has been populated using a large amount of collaboratively edited material (i.e., Wikipedia) as a knowledge source. Because of
the ever-growing size and enormous scope of Wikipedia’s coverage, the DBpedia
dataset has been increasingly applied to a wide range of web applications.
The DBpedia dataset contains a community-curated cross-domain ontology
to homogenize the description of information in the knowledge base (KB), which
is one of the largest multilingual ontologies developed to date. Version 2014 ofthis ontology covers 685 classes in total, which form a subsumption hierarchy,
and includes 2,795 different properties. This ontology has become a de facto
reference vocabulary; however, this is limited as a multilingual pivot. Althougha large number of instances among different languages are connected to theowl:sameAs1 link, matching the class level is rare. The rdfs:label properties
use language tagging to enhance multilingualism as follows.
1

https://www.w3.org/TR/owl-ref/#sameAs-def

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Eun-kyung Kim and Key-Sun Choi

<owl:Class rdf:about="http://dbpedia.org/ontology/Actor"><rdfs:label xml:lang="en">actor</rdfs:label><rdfs:label xml:lang="fr">acteur</rdfs:label><rdfs:label xml:lang="ja">俳優</rdfs:label><rdfs:label xml:lang="ko">영화인</rdfs:label>...

Number of Labeled classes

This shows that the class “Actor” has several cross-lingual corresponding terms
such as “영화인” in Korean and “Acteur” in French. Figure 1 shows the statistics of class numbers with rdfs:label properties. The number of labeled classes
for different languages varies significantly, and there is obviously an absence ofcross-lingual labeling for some editions such as Chinese. The DBpedia ontology(DBO) is continuously evolving due to its collaborative (wiki) paradigm andongoing internationalization [3, 4]. However, it suffers from a scarcity of multilingual labels, due to its derivation that is based on the popular infoboxes inEnglish. This leads to a limitation of other languages’ ability to adapt the DBO
to local language knowledge resources and makes it difficult to homogenize asa conceptual extension. Thus, identifying the global representative parts of theDBO is important for expanding multilingual ontologized space in LOD.
Figure 2 gives an overview of our motivation. Generally, the terminologicalcomponents (henceforth referred to as the TBox) of an existent ontology can betranslated and tailored to fit the understanding of other languages to expand
multilingual coverage and thus increase knowledge access across languages with600

400200GEnglis
er hmaD nutcFr henc
I h
Po talrt ianugu
Sp eseanish
Pol
R ishussi
C anhi
nese0Fig. 1. Statistics for language-labeled classes of DBpedia ontology among 10 majorlanguagesIdentifying Global Representative Classes of DBpedia Ontology
Y-­‐TBox	  X-­‐TBox	  Y-­‐ABox	  X-­‐ABox	  3

Ontological KBExpansion

Rank-basedTBox Selection

Local Ontology XX-­‐Language	  Local	  KB	  Local Ontology Y

Y-­‐Language	  Local	  KB	  Global Ontology

English	  TBox	  English	  ABox	  

Language
Adaptation

Ontological	  Space	  Local	  Space	  Fig. 2. Multilingual ontologized space expansion

existing ontologies [5]. Therefore, a multilingual pivotal ontology must accurately
represent the global common concept structure, yet remain reusable in different
languages so that connections can be made between local language knowledge
resources and ontological KBs when entering an LOD.
We aimed to identify globally representative DBO classes for different language editions in this work, based on the combination of several ranking resultsthat analyze the knowledge graph to measure the popularity of instances frommultiple perspectives. Then, a consensus global ranking could be produced via
rank aggregation; finally, we constructed a representative subset of DBO that
could capture universally popular information that would be useful for improving
the multilingual reuse of the ontology itself and would more easily and rapidly
expand the ontological domain of the local language knowledge sources. We evaluated our approach by comparing its coverage with respect to the losses causedby the selection process, which had almost the same coverage with no appreciable loss of efficiency for larger sizes when the data were adapted to multilingual
purposes.

2Rank Aggregation–based Class Selection

When determining globally representative classes of DBO, we believe that themain challenge lies in the ranking model. Figure 3 shows an overview of theproposed approach that is mainly structured as two phases, as in the following
subsections.
2.1

Language-Specific Popularity Analysis

We create a ranking model of classes to ascertain their degrees of significance inthe ontology by analyzing each language dataset individually. We first computed

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Eun-kyung Kim and Key-Sun Choi

Wikipedia

Language 1

Language 2

Language nInstance	  Ranking

Instance	  Ranking

Instance	  Ranking

PageRank	  PageRank	  PageRank	  …
Class	  Ranking

DBpedia

DBOClass	  Ranking

Instance-­‐Class	  mapping

Instance-­‐Class	  mapping

input

Rank	  Aggregation	  &	  S election	  Class	  Ranking

Instance-­‐Class	  mapping

outputRepresentative	  Subsetof	  DBO

Fig. 3. Overview of the proposed framework

the ranking order for its instances and then combined these to determine therank of a class. The rank of a class for each specific language is obtained by thePageRank [6, 7] values of its instances. We constructed a graph of instances from
Wikipedia consisting of the links between articles to calculate the ranks of theinstances. Each article corresponds to a node of the graph, and links between
articles correspond to the edges of the graph.
Then, we calculated the rank of a class by mapping information between theinstances in Wikipedia and the classes in DBpedia. We used “type” informationfrom DBpedia to map instances to classes; for example, the instance “BarackObama” is described and classified in three types as “OfficeHolder,” “Person,”and “Agent.” Our class-level ranking model characterizes the following two features of a class to determine its rank:
1. A class is more popular if it is ranked higher based on the average of its
instances’ rank scores.
2. A class is more popular if it is widely populated in DBpedia ABox (i.e., theassertional component).
We used an aggregate function (average∗counting) to compute the language
specific class-level rank CRl (C) of a class C as:nCRl (C) =p1X
P R(i) ∗ kCk,n i=1(1)

where n is the number of instances of C, P R(i) is the PageRank score of instancei, and kCk indicates the unique number of populated instances of C in DBpediaABox for a language l. In the results, CRl (C) represents the popularity of class
C in the language l edition.

Identifying Global Representative Classes of DBpedia Ontology

5

Table 1. Top 10 classes in different languages ranked by proposed approach; the distinct classes in each language are marked in bold typeEnglishFrenchPortuguese

PolishCountryCountryCountryCountry
Continent
State
Place
Place
Place
Continent
PopulatedPlace
PopulatedPlace
PopulatedPlace
Department
Agent
City
Agent
PopulatedPlace
PersonAgent
Organisation
Place
Organisation
Settlement
PersonAgentSettlement
PersonSettlement
PersonCityOrganisation
CitySettlement
ArtistRegionAmericanFootballTeam Territory
Work
AdministrativeRegion

2.2

Language-Unified Popularity Analysis

The individual ranking orders from Section 2.1 are aggregated to produce a“globally popular” order of classes that would reflect their order of importanceas judged by the collective evidence of all language editions. Table 1 depicts
the top 10 independently ranked classes in four different language editions (thefour sample languages in Figure 1). This means that different language editionsof DBpedia may have different perspectives on the information that they contain. We produced the consensus rank for each language-specific ranking orderusing the existing score-based rank aggregation method (i.e., the Borda countmethod [8]). The Borda count is one of the most well-known and intuitive rankaggregation schemes in which each element for each ranking order is given ascore depending on its rank, and these weights are then summed across all suchranking orders.
Each language-specific ranking is associated with a finite set of m classes
C = {C1 , ..., Cm }, each of which is given a score depending on its place in theindividual ranking order, the Borda scores are summed for all such individual
scores to compute their total score. More formally, each class Ci has a different
ranked position x, which is based on the class ranking function CRl (Ci ). We
then define τ lj (Ci ) = x (1 ≤ x ≤ m, 1 ≤ j ≤ n) such that the jth language
edition ranks the class Ci at the xth position. Each class Ci has a Borda-based
global ranking CRg (Ci ), defined as:CRg (Ci ) =nX(m − τ lj (Ci )).

(2)

j=1

TBox Selection: After computing each class’s global rank, the classes withthe higher order global ranking scores are selected as the representative R witha certain size ρ by the following to define the classes and properties that should
be included in:
Definition 1 The set of classes C(R) contains a class C iff:6

Eun-kyung Kim and Key-Sun Choi

– C is a class and CRg (C) ≥ CRg (Cρ ) or– C is a class and there is a class D ∈ C(R) such that D v C
Definition 2 The set of properties P (R) contains a property p iff p is a property
belonging to a class C ∈ C(R).

3

Experimental Analysis

We measured the coverage of the representative subset; good representatives
are expected to capture most of the information in the initial dataset without
much loss. We compared the performance of our algorithm with two others:
Monolingual-Rank (Mono) and Random-Selection (Rand). Mono is an approachthat uses only English to calculate the rank computation. Rand represents theaverage performance of 10 runs by randomly selecting a subset of the dataset asa representative.
We used the DBpedia Mapping-based Dataset (2014) in our evaluation, which
is a set of assertion triples that contain very specific information about theentities that can be used to query Wikipedia. Every instance in those triples isclassified by the classes of DBO, and all properties are defined in the ontology. Asample RDF statement (in triple form: < s, p, o >) of this dataset that pertains
to “Barack Obama” is as follows.
PREFIX dbo: http://dbpedia.org/ontology/
PREFIX dbr: http://dbpedia.org/resource/<dbr:Barack_Obama, dbo:birthPlace, dbr:Hawaii>This shows that the resource “Barack Obama” is the subject of other statements and presents a triple describing “Barack Obama”s birthplace as Hawaii.
We vary the number of representative subsets from 1 to 562 (the numberof all classes involved in the rank; nearly 18% of the DBO’s classes are neverused in any languages) and compare the coverage achieved by the three methods
listed in Table 2. It is clear from the results that the extracted globally popularclasses have helped realize higher coverage for many languages.
3.1

Evaluations

We used gold standards2 that were derived by assuming possible characteristics from both the number of the existing rdfs:label and the persistence ofthe classes. Then, we extracted a subset of DBO that could be adapted as thegroundwork for automatic DBpedia mapping among languages through experimental evaluation.
2

Evaluation data for this work is available for download at
http://semanticweb.kaist.ac.kr/home/index.php/DBBO

Identifying Global Representative Classes of DBpedia Ontology

7

Table 2. Coverage of representative set for ten languages defined in Figure 1. Percentages of triples are defined by classes in Ours, Mono, and Rand. |R| represents the sizeof the selected classes|R| Ours Mono Rand
1 27.85% 0.85% 0.03%
5 67.54% 39.69% 0.03%
10 80.90% 67.54% 0.03%
20 84.54% 83.99% 2.26%
50 91.39% 84.60% 6.57%
100 93.05% 91.61% 12.96%
200 94.27% 92.31% 27.51%
300 94.74% 94.56% 42.35%
400 96.86% 94.74% 57.98%
500 97.13%-72.30%562 97.18%-83.02%For the first evaluation, we assumed that the classes that already have crosslingual labels in many languages are important, because the labeling effort indicates their potential reuse in other languages. We create a gold standard basedon this assumption by calculating “the number of existing rdfs:labels (Goldstandard 1)” for each class of DBO and divided the classes into two sets, positive
and negative, in accordance with this assumption. 1) Positive set: Classes containing five (the mean value of DBO) or more labels. 2) Negative set: Classes
containing fewer than five labels.
For the other evaluation, we assumed that the classes that are preserved
across the two versions (the first and latest DBOs) are more important than thenewly added classes in the 2014 version called “persistence (Gold-standard 2).”
We divided the classes into the following two sets. 1) Positive set: Classes in both
DBO 3.2 and DBO 2014. 2) Negative set: Classes that are only in DBO 2014.
Figure 4 shows the F1 scores with respect to Gold-standard 1 and Gold-standard
2 as binary classifications. Based on these results, the size (ρ) of the multilingual
pivotal ontology is set to 260 to obtain the best F1 score performance from thetwo evaluations. It is possible to reduce the size and hierarchy to only 260 fromthe top of the final order as a basis for the total of 685 classes. In comparison withTable 2, this smaller ontology may have at least approximately 90% coverage ofABox.

8Eun-kyung Kim and Key-Sun Choi
60

F1 score (%)

60

40
40

20

20

0100

200

300
|R|

400

500

600

0100

200

300

400500600|R|

Fig. 4. F1 scores of Gold-standard 1 (the left side) and Gold-standard 2 (the rightside) with respect to |R|

4Conclusion

We presented an approach for identifying global representative classes from DBpedia ontology (DBO), regarded as a multilingual pivotal ontology in this work.
We combined the different independently constructed preferences of ranks foreach language edition of Wikipedia to produce a consensus order of classes for
DBO that is more desirable for representing the knowledge base for multilingual reuse and for connectability as Linked Open Data (LOD) through ontology.
Our experimental results showed that the proposed approach significantly helped
improve the labeling performance for non-English languages compared to bothmonolingual and random methods; the selected classes can be smaller than theentire ontology without significant loss of coverage. We expect that a representative subset of DBO in this paper will have a central role in the enrichment
and integration of local language knowledge resources in LOD, avoiding islandsof monolingual data.

Acknowledgements
This work was supported by Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIP)(No.
R0101-16-0054,WiseKB: Big data based self-evolving knowledge base and reasoning platform); the Bio & Medical Technology Development Program of theNRF funded by the Korean government, MSIP(2015M3A9A7029735).

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