=Paper=
{{Paper
|id=Vol-1545/oaei15_paper11
|storemode=property
|title=LYAM++ results for OAEI 2015
|pdfUrl=https://ceur-ws.org/Vol-1545/oaei15_paper11.pdf
|volume=Vol-1545
|dblpUrl=https://dblp.org/rec/conf/semweb/TigrineBT15
}}
==LYAM++ results for OAEI 2015==
https://ceur-ws.org/Vol-1545/oaei15_paper11.pdf
LYAM++ Results for OAEI 2015
Abdel Nasser Tigrine, Zohra Bellahsene, Konstantin Todorov
{lastname}@lirmm.fr
LIRMM / University of Montpellier, France
Abstract. The paper presents a novel technique for aligning cross-lingual
ontologies that does not rely on machine translation, but uses the large
multilingual semantic network BabelNet as a source of background knowl-
edge. In addition, our approach applies a novel orchestration of the com-
ponents of the matching workflow. We demonstrate that our method
outperforms considerably the best techniques in the state-of-the-art.
1 Presentation of the system
In spite of the considerable advance that has been made in the field of on-
tology matching recently, many questions remain open [1]. The current work
addresses the challenge of using background knowledge with a focus on aligning
cross-lingual ontologies, i.e., defined in different natural languages [2].
Indeed, considering multilingual and cross-lingual information is becoming
more and more important, in view particularly of the growing number of content-
creating non-English users and the clear demand of cross-language interoperabil-
ity. In the context of the web of data, it is important to propose procedures for
linking vocabularies across natural languages, in order to foster the creation of
a veritable global information network.
The use of different natural languages in the concepts and relations labeling
process is becoming an important source of ontology heterogeneity. The methods
that have been proposed to deal with it most commonly rely on automatic
translation of labels to a single target language [3] or apply machine learning
techniques [2]. However, machine translation tolerates low precision levels and
machine learning methods require large training corpus that is rarely available
in an ontology matching scenario. An inherent problem of translation is that
there is often a lack of exact one-to-one correspondence between the terms in
different natural languages.
1.1 State, purpose, general statement
We present LYAM++ (Yet Another Matcher - Light), a fully automatic cross-
lingual ontology matching system that does not rely on machine translation.
Instead, we make use of the openly available general-purpose multilingual se-
mantic network BabelNet1 in order to recreate the missing semantic context in
1
http://babelnet.org/
2 N. Tigrine, Z. Bellahsene, K. Todorov
Fig. 1: The processing pipeline of LYAM++.
the matching process. Another original feature of our approach is the choice of
orchestration of the matching workflow. Our experiments on the MultiFarm2
benchmark data show that (1) our method outperforms the best approaches in
the current state-of-the-art and (2) the novel workflow orchestration provides
better results compared to the classical one.
1.2 Specific techniques used
The workflow of LYAM++ is given in Fig. 1. We take as an input a source
ontology S, given in a natural language lS and a target ontology T , given in
a language lT . The overall processes consists of four main components: a ter-
minological multilingual matcher, a mapping selection module and, finally, a
structural matcher. One of the original contributions of this work is the choice
of orchestration of these components. Indeed, the places of the mapping selec-
tion module and the structural matcher are reversed in the existing OM tools
[4]. However, we wanted to ensure that we feed only good quality mappings to
the structural matcher, therefore we decided to filter the discovered correspon-
dences right after producing the initial alignment. This decision is supported
experimentally in the following section.
The multilingual terminological matching module, the second contribution
described in this paper, acts on the one hand as a preprocessing component and,
on the other hand – as a light-weight terminological matcher between cross-
lingual labels. We start by splitting the elements of each ontology in three groups:
labels of classes, labels of object properties and labels of data object properties
(in colors blue, black and red in the figure), since these groups of elements
are to be aligned separately. A standard preprocessing procedure is applied on
these sets of labels, comprising character normalization, stop-words filtering,
tokenization and lemmatization. The tokens of the elements of T are then aligned
to BabelNet. At first, every token of a given label s in S is enriched by related
2
http://web.informatik.uni-mannheim.de/multifarm/
Cross-lingual Ontology Matching 3
terms and synonyms from BabelNet and all of these terms are represented in the
language lT , which makes these terms comparable to the tokens of the labels in
T . A simple similarity evaluation by the help of the Jaccard coefficient selects
the term in each set of related terms corresponding to a given token from s that
has the highest score with respect to every token in each label of T . This helps
to restitute the label s in the language lT . Finally, the labels in each group of S
and T , seen as sets of tokens, are compared by using the Soft TFIDF similarity
measure [5], which produces an intermediate terminological alignment.
The three remaining components are standard OM modules [4], although
ordered in a new manner. The Mapping selection is a module that transforms the
initial 1 to many mapping to a 1:1 alignment based on the principle of iteratively
retaining the pairs of concepts with maximal value of similarity. Finally, the
structural matcher component filters the trustworthy pairs of aligned concepts
by looking at the similarity values produced for their parents and their children
in the ontology hierarchies.
1.3 Link to the system and parameters file
The system is not yet available online. The reason for that is that it depends
heavily on the use of BabelNet, which is a protected source. We are working
on implementing a sharable version of LYAM++ making use of different open
access background knowledge sources.
1.4 Link to the set of provided alignments (in align format)
The alignments produced by LYAM++ for this year’s Multifarm track can be
found under the following link: http://www.lirmm.fr/benellefi/Lyam++.rar
2 Results
We have evaluated our approach on data coming from the ontology align-
ment evaluation initiative (OAEI)3 and particularly Multifarm—a benchmark
designed for evaluating cross-lingual ontology matching systems. Multifarm data
consist of a set of 7 ontologies originally coming from the Conference benchmark
of OAEI, translated into 8 languages. Two evaluation tasks are defined: task 1
consists in matching two different ontologies given in different languages, while
task 2 aims to align different language versions of one single ontology.
We have performed experiments on both tasks by using the pairs of languages
given in the summary of our results in Table 1.
In another experiment, we have evaluated the results obtained by using our
novel orchestration of matching components, as compared to the standard or-
chestration. The figures in Table 2 show that the workflow proposed in this paper
acts in favor of achieving better results as compared to the standard method.
3
http://oaei.ontologymatching.org/
4 N. Tigrine, Z. Bellahsene, K. Todorov
Table 1: Comparing LYAM++ to AML
Lang. FR-RU FR-PT FR-NL ES-FR ES-RU ES-PT ES-NL EN-PT EN-RU EN-FR
pair
LYAM++ 0.54 0.58 0.62 0.60 0.60 0.60 0.63 0.67 0.53 0.59
Average F-measures over all threshold values per language pair for task 1.
Lang. FR-RU FR-PT FR-NL ES-FR ES-RU ES-PT ES-NL EN-PT EN-RU EN-FR
pair
LYAM++ 0.58 0.72 0.67 0.77 0.64 0.70 0.68 0.74 0.59 0.85
Average F-measures over all threshold values per language pair for task 2.
Table 2: Comparing the standard and the novel orchestrations
Language pair EN-FR EN-RU ES-FR
Standard (avg) 0.45 0.32 0.39
Novel (avg) 0.84 0.59 0.76
Average F-measures over all threshold values per language pair.
Threshold Value 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
Standard (avg) 0.42 0.42 0.42 0.42 0.42 0.39 0.32 0.20
Novel (avg) 0.78 0.78 0.78 0.78 0.78 0.75 0.65 0.4
Average F-measures over all language-pairs per threshold value.
3 Discussions on the way to improve the proposed system
Currently, we are working on enhancing the system in order to make appli-
cable to the general ontology matching problem and not only to cross-lingual
ones. We have generated first results on the Conference benchmark without any
modification in the settings and our results are quite promising. For the majority
of the datasets (ontology pairs) our system achieves a f-score almost as good as
the f-score of AML, the best performing system on that track.
We consider that a key feature for the improvement of our system is the
appropriate choice of background knowledge. In order to improve the results
achieved on the Conference track, we plan to use monolingual general purpose
background knowledge (for example, the english subgraphs of YAGO or DBPe-
dia) instead of BabelNet.
We intend to use domain specific background knowledge in order to solve
alignment problems in specific areas of knowledge. More precisely, we plan to
participate on the Anatomy track by testing different kinds of domain specific
background knowledge, such as UMLS or other.
4 Conclusions
We presented an efficient approach for aligning cross-lingual ontologies by
using the multilingual lexical database BabelNet. Subjects of ongoing and future
work are (1) testing and evaluating different sources of external knowledge, (2)
applying the approach to a larger set of languages and (3) adaptation of the
Cross-lingual Ontology Matching 5
approach to the monolingual case and studying the use of background knowledge
in a monolingual ontology matching scenario.
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