=Paper=
{{Paper
|id=Vol-2032/oaei17_paper3
|storemode=property
|title=CroLOM results for OAEI 2017: summary of cross-lingual ontology matching systems results at OAEI
|pdfUrl=https://ceur-ws.org/Vol-2032/oaei17_paper3.pdf
|volume=Vol-2032
|authors=Abderrahmane Khiat
|dblpUrl=https://dblp.org/rec/conf/semweb/Khiat17
}}
==CroLOM results for OAEI 2017: summary of cross-lingual ontology matching systems results at OAEI ==
https://ceur-ws.org/Vol-2032/oaei17_paper3.pdf
CroLOM results for OAEI 2017
Summary of Cross-Lingual Ontology matching Systems at OAEI
Abderrahmane Khiat
Human-Centered Computing Lab, Freie Universität Berlin, Germany
abderrahmane.khiat@fu-berlin.de, abderrahmane khiat@yahoo.com
Abstract. This paper presents the results obtained in the OAEI 2017 campaign
by our ontology matching system CroLOM. CroLOM is an automatic system
especially designed for aligning multilingual ontologies. This is our second par-
ticipation with CroLOM in the OAEI and the results have so far been positive.
Keywords: Cross lingual Alignment, Multilingual Ontologies Survey, Ontology
Matching, Yandex, Semantic Similarity, OAEI, Direct matching.
1 Introduction
With the growing number of ontologies defined in different languages, multilingualism
has become an issue of major interest in ontology matching field. Multilingual ontol-
ogy alignment, defined as the process of identification of semantic correspondences
between entities of different ontologies described in different natural language, repre-
sents the solution to the problem of semantic interoperability between different sources
of distributed information [1, 2]. Several methods have been elaborated to semantically
align multilingual ontologies. These methods can be generally split into two main cate-
gories direct and indirect matching approaches [3]. The approaches of the first category
are based on external resources (i.e. translation) to align cross-lingual ontologies. How-
ever, the approaches of the second category are based on the composition of alignments
such as the work proposed in [4] where the authors reuse the mappings between ontolo-
gies that already exist.
In this study, we consider the approaches of the first category, since we develop an
approach which implements a direct strategy. However, there are many questions re-
garding this approach to address the multilingualism issue. These questions are as fol-
lows: (1) Which machine translation should be used, (2) which translation path should
be considered and (3) which ontologies features and dictionaries can be exploited. In
the following paragraphs, we describe the points mentioned above.
First, several translators have been developed to translate automatically the text from
one natural language to another. We can mention for example: Google, Bing, SDL and
Gengo translators. Each translator has its specific characteristics such as: number of
source/target languages and execution time. However, selecting one or several transla-
tors (by combining them) remains an open problem. This choice is crucial in ”direct
approaches”, since they apply a monolingual matching techniques in cross-lingual on-
tology mapping.
Second, the translation path also plays an important role to resolve the heterogeneity
problem. Two translation paths can be considered, (i) either considering the translation
path from one to another or (ii) selecting a pivot language which is often the English
language. This choice highly depends on available sources (dictionaries, thesaurus, etc.)
in different natural languages. Most matching systems consider the translation path
using English as a pivot language due to available sources in English language.
Finally, in some cases, the results of a translation machine could be poor, however,
to avoid this situation some ontology features can be exploited such Description Logics.
Most matching systems which implement a direct translation approach uses a well-
known translators mentioned above. The current work uses also a direct matching ap-
proach. However, unlike existing approaches, it addresses the multilingualism challenge
by using (a) the Yandex translator1 , (b) a translation into a pivot language after apply-
ing NLP and (c) a similarity computation based on the categories of the words and
synonyms.
The rest of the paper is organized as follows. First, in Section 2, we discuss the
top systems that participated in the last editions of the multifarm track. In section 3 we
describe the CroLOM system. Section 4 contains the experiment results. Finally, some
concluding remarks and future work are presented in Section 5.
2 Related Work
In this section, we continue our previous work [5, 20] by covering the main cross-lingual
ontology matching systems that have participated in the last editions of the Multifarm
track of OAEI evaluation campaign, we should note that the Multifarm track includes
the Arabic dataset [5, 6] since 2015. Most of systems which participated at OAEI use a
direct translation-based matching approach.
Table 1 summarizes the results of the systems achieving the best results in the Multi-
farm track in previous edition. Note that some of these systems participated in different
editions and they obtained low results due to problems such as parsing or accessing to
translator server. These results also includes the changes that have been made on Mul-
tifarm track. The purpose of these selection is to observe the best results obtained on
Multifarm track.
The AUTOMSv2 system [14] uses a free Java API named WebTranslator2 in order
to solve the multi-language problem by translating label and properties in English lan-
guage. The GOMMA system [15] uses a free translation API named ”mymemory”3
to automatically translate non-English terms. The WeSeE-Match system [16] trans-
lates the fragments, labels, and comments in English as a pivot language using the
Bing4 Search APIs translation capabilities. The WikiMatch system [17] employs the
1
https://translate.yandex.com/?lang=es-en&text=administrar&
ncrnd=5317
2
http://webtranslator.sourceforge.net/
3
http://mymemory.translated.net/
4
https://www.microsoft.com/en-us/translator/translatorapi.aspx
Table 1: Top systems in the multifarm track
OAEI Top Systems Multifarm Track Precision F-measure Recall
2012 AUTOMSv2 without Arabic 0.49 0.36 0.10
2012 WeSeE // 0.61 0.41 0.32
2012 GOMMA // 0.29 0.31 0.36
2012 WikiMatch // 0.34 0.27 0.23
2013 YAM++ // 0.51 0.40 0.36
2014 AML // 0.57 0.54 0.53
2014 LogMap // 0.80 0.40 0.28
2014 XMap // 0.31 0.35 0.43
2015 AML 0.53 0.51 0.50
2015 LogMap 0.75 0.41 0.29
2015 XMap 0.23 0.25 0.28
2015 CLONA 0.46 0.39 0.35
2016 CroLOM 0.55 0.36 0.28
2017 KEPLER 0.43 0.31 0.25
2017 Wikiv3 0.30 0.25 0.21
Google Translation API5 for addressing multi-lingual ontologies. The CLONA system
[18] translates the entities described in different natural languages into English as a
pivot language using Bing translator. Then it uses Lucene search engine and WordNet
to determine alignment candidates. The XMap system [7] uses an automatic translation
for obtaining correct matching pairs in multilingual ontology matching. The transla-
tion is done by querying Microsoft Translator for the full name. The AML system [8]
uses an automatic translation module based on Microsoft Translator. The translation is
done by querying Microsoft Translator for the full name (rather than word-by-word).
To improve performance, AML stores locally all translation results in dictionary files,
and queries the Translator only when no stored translation is found. The LogMap sys-
tem that participated in the OAEI 2014 campaign used a multilingual module based on
Google translate; however the new version of the LogMap system uses both Microsoft
and Google translator APIs [11]. The YAM++ system [9] uses a multilingual transla-
tor based on Microsoft Bing to translate the annotations to English. The KEPLER and
Wikiv3 systems participated for the first time at OAEI2017....
We have also observed that, at OAEI2017 the best results are still those obtained by
AML system in 2015, achieving an F-measure equals to 0.51. This is surprising, in spite
of many research works that have been established in the field of multilingual ontology
matching.
5
http://code.google.com/apis/language/translate/overview.html
3 CroLOM: Cross-Lingual Ontology Matching System
We summarize the process of our approach to provide a general idea of the proposed
solution. It consists in the following successive phases:
3.1 Extraction and Normalization
CroLOM extracts first the entities of the input ontologies. Then, it employs NLP to nor-
malize the entities described in different natural languages. Unlike existing approaches,
we have applied lemmatization, stemming and stopword elimination for each natural
language separately before translation step. First, for each language considered by mul-
tifarm, we have established the stop words of each language in order to eliminate them
from entities labels. Second, we have developed morphological algorithms to obtain
lemmatization of the entities words.
This step is important 6 , since one of matchers used is (1) based on string compar-
ison algorithm to compute similarity and (2) the categories of the words are stoked in
lemma form.
3.2 Translation
Once the entities are normalized, CroLOM uses the Yandex translator in order to trans-
late the entities described in different natural languages in English as a pivot language.
After translation, CroLOM employs for the second time the normalization step in order
to eliminate the stop words of the English language from entities labels.
We have mentioned before that the translation path and the used translator play im-
portant role to resolve the multilingualism heterogeneity problem. Our choice for the
Yandex translator is justified by the fact that it is ranked as the 4th largest search engine
in the world and it has not previously used to align multilingual ontologies. However, we
have chosen English as a pivot language because there a lot dictionaries that are avail-
able in English language. These dictionaries could be exploited in order to improve our
system in the future. In addition, to compute the similarity between entities, we have
used dictionaries (word categories and WordNet) in English. Due to automatic transla-
tion, we have observed that some stop words can be appeared in translated entities. For
this purpose, we have employed the normalization for the second time.
3.3 Similarity Computation
Once the translation and standardization are carried out, CroLOM applies first, a case
conversion by converting all entities words in lower case then it passes to the simi-
larity computation step. Unlike existing systems, which use well known matchers, we
have developed a matcher which calculates the similarity between entities based on the
categories of the Words, string-based algorithm and synonyms using Wordnet7 .
6
This step allows to obtain good results such as the results of our previous work [19] (STRIM
system) in instance matching.
7
http://wordnet.princeton.edu/
The matcher developed establishes a Cartesian product between the two entities
words, then it returns the maximum similarity value using Levenshtein distance, simi-
larity based on WordNet and similarity based on the categories of the words. The sim-
ilarity based on the categories of the words has been adapted with some modification
from the project ”Calculate Semantic Similarity” 8 . The project has been developed to
match sentences, however we have modified the code in order to compute similarity
between words.
3.4 Alignment Identification
Finally, CroLOM applies a filter to select candidate correspondences which possess the
maximum similarity value in each line of Cartesian product between entities. Then it
applies a second a filter to identify the correspondences that possess similarity value
upper than a given threshold.
4 Experimental Study
The results obtained by running our CroLOM system on multifarm track of OAEI2017
are the same as in OAEI2016 since we partipated with the same version. The re-
sults are available at the following website: http://oaei.ontologymatching.
org/2017/results/multifarm/index.html.
5 Conclusion
In this paper, we have presented our CroLOM system, a cross-lingual ontology match-
ing system. CroLOM unlike existing approaches, applies first NLP on each natural lan-
guage before translation. Then, it uses the Yandex translator in order to translate all en-
tities in English as pivot language. Finally, CroLOM computes the similarity between
translated entities based on the category of the words and WordNet, hybridizing statistic
and semantic similarity.
As future challenges, we aim to (1) improving the quality results of our system and
especially the execution time, (2) conduct a survey study that addresses all the issues
mentioned above, (3) taking into account the indirect approaches.
References
1. A. Khiat and M. Benaissa, ”A New Instance-Based Approach for Ontology Alignment”.
International Journal on Semantic Web and Information Systems (IJSWIS), Vol. 11, No. 3,
ISSN 1683-3198, 2015.
2. A. Khiat and M. Benaissa, ”Boosting Reasoning-Based Approach by Structural Metrics for
Ontology Alignment”. The Journal of Information Processing Systems (JIPS), 2015.
8
https://sourceforge.net/projects/semantics/
3. S Zhang and O. Bodenreider, ”Alignment of Multiple Ontologies of Anatomy: Deriving Indi-
rect Mappings from Direct Mappings to a Reference”, AMIA 2005 Symposium Proceedings,
2005.
4. J. J. Jung, A. Hakansson, and R. H. . ”Indirect Alignment between Multilingual Ontologies:
A Case study of Korean and Swedish Ontologies,” in Proceedings of the Third KES Interna-
tional Symposium on Agent and Multi-Agent Systems: Technologies and Applications,2009.
5. A. Khiat and M. Benaissa and Ernesto Jimnez-Ruiz ”ADOM: arabic dataset for evaluating
arabic and cross-lingual ontology alignment systems”. In Proceedings of the 10th Interna-
tional Workshop on Ontology Matching co-located with the 14th International Semantic Web
Conference (ISWC 2015), USA, 2015.
6. A. Khiat, G. Diallo, B. Yaman, E. Jimnez-Ruiz and M. Benaissa, ”ABOM and ADOM:
Arabic Datasets for the Ontology Alignment Evaluation Campaign”. In Proceedings of the
14th International Conference (ODBASE 2015), Greece, 2015.
7. W. Djeddi, M. T.Khadir and S. Ben-Yahia, “XMap++ results for OAEI 2015”. In Proceedings
of the 10th International Workshop on Ontology Matching ISWC 2015, USA, 2015.
8. D. Faria, C. Martins, A. Nanavaty, D. Oliveira, B. Sowkarthiga, A. Taheri, C. Pesquita, F.
Couto and I. Cruz , “AML results for OAEI 2015”. In Proceedings of the 10th Workshop on
Ontology Matching ISWC 2015, USA, 2015.
9. D. Ngo and Z. Bellahsene, “YAM++ results for OAEI 2013”, In Proceedings of the 8th
Workshop on Ontology Matching ISWC 2013, pp. 211-218, Australia, 2013.
10. A. Khiat and M. Benaissa, “AOT / AOTL results for OAEI 2014”. In Proceedings of the 9th
International Workshop on Ontology Matching ISWC 2014, pp. 113-119, Italy, 2014.
11. E. Jiménez-Ruiz, BC. Grau, A. Solimando, V. Cross, “LogMap family results for OAEI
2015”. In Proceedings of the 10th Workshop on Ontology Matching ISWC 2015, USA, 2015.
12. O. Svab, V. Svatek, P. Berka, D. Rak and P. Tomasek, “OntoFarm: Towards an Experimental
Collection of Parallel Ontologies”, In: Poster Track of ISWC 2005, Galway, 2005.
13. C. Meilicke, R. Garca-Castro, F. Freitas, WR. Van Hage, E. Montiel-Ponsoda, R.R. De
Azevedo, H. Stuckenschmidt, O. vb-Zamazal, V. Svtek and A. Tamilin, “MultiFarm: A
benchmark for multilingual ontology matching”. Web Semant. Sci. Serv. Agents World Wide
Web. Vol. 15, pp. 62-68, 2012.
14. K. Kotis, A. Katasonov and J. Leino, ”AUTOMSv2 results for OAEI 2012”, In Proceedings
of the 7th Workshop on Ontology Matching ISWC 2012, USA, 2012.
15. A. Gro, M. Hartung, T. Kirsten and E. Rahm, ”GOMMA results for OAEI 2012”, In Pro-
ceedings of the 7th Workshop on Ontology Matching ISWC 2012, USA, 2012.
16. H. Paulheim, ”WeSeE-Match results for OEAI 2012”, In Proceedings of the 7th Workshop
on Ontology Matching ISWC 2012, USA, 2012.
17. S. Hertling and H. Paulheim, ”WikiMatch results for OEAI 2012”, In Proceedings of the 7th
Workshop on Ontology Matching ISWC 2012, pp., USA, 2012.
18. M. El-Abdi, H. Souid, M. Kachroudi and S. Ben-Yahia, ”CLONA results for OAEI 2015”,
In Proceedings of the 10th Workshop on Ontology Matching ISWC 2015, USA, 2015.
19. A. Khiat, M. Benaissa and M. A. Belfdhal, ”STRIM results for OAEI 2015 instance matching
evaluation”. In Proceedings of the 10th International Workshop on Ontology Matching co-
located with the 14th International Semantic Web Conference (ISWC 2015), USA, 2015.
20. A. Khiat, CroLOM: Cross-Lingual Ontology Matching System Results for OAEI 2016. In
Proceedings of the 12th International Workshop on Ontology Matching co-located with the
15th International Semantic Web Conference (ISWC 2016), Japan, 2016.