=Paper=
{{Paper
|id=Vol-1486/paper_6
|storemode=property
|title=Supporting Multilingual Ontology Matching With MoKi
|pdfUrl=https://ceur-ws.org/Vol-1486/paper_6.pdf
|volume=Vol-1486
|dblpUrl=https://dblp.org/rec/conf/semweb/DragoniP15
}}
==Supporting Multilingual Ontology Matching With MoKi==
https://ceur-ws.org/Vol-1486/paper_6.pdf
Supporting Multilingual Ontology Matching With MoKi
Mauro Dragoni and Giulio Petrucci
FBK–IRST, Trento, Italy
dragoni|petrucci@fbk.eu
Abstract. Multilingual ontology matching is a recent research topic concerning
the application of the traditional schema matching algorithms in conjunction with
the use of multilingual resources. In this demo, we present an architecture, based
on the use of Information Retrieval (IR) techniques, developed for addressing the
task of suggesting alignments between two or more multilingual ontologies. The
validation performed on domain-specific use cases demonstrated the suitability
of the presented system.
1 Introduction
Ontology matching [5][1] is a task that has attracted considerable attention in recent
years. However, with very few exceptions, research in this field has primarily focused
on the development of monolingual matching algorithms. As more and more artifacts,
especially in the Linked Open Data realm, become available in a multilingual fashion,
novel matching algorithms are required. With the growth of interest in creating multilin-
gual semantic artifacts for representing knowledge, the possibility of defining effective
links between different artifacts allows to spread knowledge not only for enriching the
representation of the knowledge itself, but also for breaking language barriers in ac-
cessing contents.
In the case of a multilingual environment, there are some peculiarities that can be
exploited in order to relax the classic alignment task:
– the use of multilinguality allows to reduce the problems raised when two different
concepts have the same label;
– multilingual resources provide term translations that have already been adapted to
the represented domains ([6]);
In this demo, we present a work exploiting the two aspects described above in order
to build a multilingual term-based approach for defining mappings between multilingual
ontologies. Such an approach has been evaluated on domain-specific (agriculture and
medical domains) use cases in order to measure both the efficiency of the tool and the
effectiveness of the algorithm in a real-world environment.
2 The IR-based Approach For Multilingual Ontology Matching
Below, we described the procedure followed for implementing the system and the strat-
egy that we used for defining new matches given two ontologies.
Representation of Information The approach is based on the exploitation of textual
information (labels) associated with each concept described in an ontology. Where, with
the term “label”, we mean a string identifying the concept associated with its language
tag (i.e. “concept label@lang code”)
This way, we are able to exploit, for each entity modeled in the ontology, all labels in
all languages by making string-based solutions both effective and semantically sensible.
Indeed, when a particular label (independently by the language) is chosen by experts
during the creation of an ontology, they implicitly inject in this choice their knowledge
about the equivalence of meanings between different translations of each label.
Index Construction For each entity defined in the ontology, we have a set of pairs
“label-language”, and, in case of synonymy, we may have more pairs for the same lan-
guage. Such labels are tokenized, stemmed , and normalized by using natural language
process libraries. After this process, each pair is stored in an inverted index [7].
For instance, by considering a concept “Activity” having three labels in three differ-
ent languages, its representation will be like it is shown below:
[prefLabel] "activity"@en --> label-en:activity
[prefLabel] "attivit"@it --> label-it:attivit
[prefLabel] "actividad"@es --> label-es:actividad
Matches definition Once the index representing and ontology (called “target”) is cre-
ated, the definition of new matches is done by performing queries using multilingual
information extracted from the entities defined in the other ontology (called “source”).
Such a query is created by building a structured representation of information defined in
the concept contained in the source ontology that needs to be mapped. Therefore, sim-
ilarly to the creation of the indexed records, by considering the example shown above,
the corresponding query is built as follows:
[prefLabel] "executable activity"@en; "attivit eseguibile"@it; "actividad ejecutable"@es
the created query will be:
label-en:"executable activity" OR label-it:"attivit eseguibile" OR label-es:"actividad ejecutable")
When the system receives the request for trying to create a new match, it performs a
search operation through the records built during the indexing phase. As a result, a rank
ordered by the retrieval status value (RSV) is produced and returned by the system.
Further details about the algorithms used in this approach may be found in [3,2]
3 MoKi Implemented Facilities
The back-end component described in the previous Section can be accessed through the
user facilities that have been integrated as extensions of the MoKi [4] tool and it can be
used through the set of user facilities briefly described below.
For managing the mappings, a dedicated section in the concept modeling page as
been integrated as shown in Figure 1. Here, the expert is able to see which are the
concepts that have been already mapped with the current one and to decide if to maintain
such mappings or to remove them. For creating a new mapping, the expert has to choose
which ontology to use for requesting mapping suggestions, and then to click on the
“Add New Mapping” button for invoking the suggestion service.
Fig. 1: User facility for invoking the suggestion service.
When the request is sent, on the background the structured representation of the
current concept is converted into a query (as described in Section 2) which is performed
on the index containing the concepts of the ontology specified by the expert. When the
rank of the suggestions is composed, it is proposed to the expert as shown in Figure 2.
For each suggestion, ordered by confidence score, the expert is able to open the concept
description page (if available) by clicking on the concept URI, and to eventually define
a new mapping by clicking on the “Create Mapping” button.
Fig. 2: Example of rank produced by the IR system containing the five suggestions for
mapping the concept “Learning” defined in the Organic.Lingua ontology with a concept
coming from the Agrovoc one.
Besides this, in a separated module, experts are able to upload new ontologies and
creating the related indexes. Figure 3 shows the interface used by the experts for up-
loading a new ontology in the repository. This facility allows to convert the uploaded
ontology in the structured representation described in Section 2 and to store it in a ded-
icated index. From this interface, experts are able to manage the ontologies already
stored in the repository by viewing some basic information about them and, eventually,
to delete one or more ontologies.
For adding a new ontology to the repository, experts have to select the file containing
the ontology, write a description, decide an acronym for referring the ontology in the
other sections of the tool, and, finally, press the “Save” button.
4 Concluding Remarks
The presented system has been tested on a set of domain-specific multilingual ontolo-
gies (Eurovoc, Agrovoc, and Gemet for the agriculture domain, and MDR, MeSH, and
SNOMED for the medical domain). Table 1 presents the obtained results. The reported
precision has been measured after the analysis of the first X elements contained in the
Fig. 3: Interface used for loading a new ontology within the system.
rank, respectively 1, 3, and 5, in order to show how the effectiveness of the system
changes by varying the number of suggestions considered. The obtained results demon-
strated the suitability of the proposed system for supporting the multilingual ontology
matching task.
Mapping Set # of Mappings Prec@1 Prec@3 Prec@5 Recall
Eurovoc → Agrovoc 1297 0.816 0.931 0.967 0.874
Agrovoc → Eurovoc 1297 0.906 0.969 0.988 0.695
Gemet → Agrovoc 1181 0.909 0.964 0.983 0.546
Agrovoc → Gemet 1181 0.943 0.981 0.994 0.740
MDR → MeSH 6061 0.776 0.914 0.956 0.807
MeSH → MDR 6061 0.716 0.888 0.939 0.789
MDR → SNOMED 19971 0.621 0.826 0.908 0.559
SNOMED → MDR 19971 0.556 0.760 0.855 0.519
MeSH → SNOMED 26634 0.690 0.871 0.931 0.660
SNOMED → MeSH 26634 0.657 0.835 0.908 0.564
Table 1: Results obtained on a set of multilingual ontologies.
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