=Paper=
{{Paper
|id=Vol-1171/CLEF2005wn-adhoc-GuyotEt2005
|storemode=property
|title=Ontology-Based Multilingual Information Retrieval
|pdfUrl=https://ceur-ws.org/Vol-1171/CLEF2005wn-adhoc-GuyotEt2005.pdf
|volume=Vol-1171
|dblpUrl=https://dblp.org/rec/conf/clef/GuyotRF05a
}}
==Ontology-Based Multilingual Information Retrieval==
https://ceur-ws.org/Vol-1171/CLEF2005wn-adhoc-GuyotEt2005.pdf
Ontology-Based Multilingual Information Retrieval
Jacques Guyot* Saïd Radhouani*,** Gilles Falquet*
*
Centre universitaire d’informatique
24, rue Général-Dufour, CH-1211 Genève 4, Switzerland
**
Laboratoire CLIPS-IMAG, B.P. 53, 38041 Grenoble cedex 9, France
Jacques.Guyot@rolex.com, {Said.Radhouani, Gilles.Falquet}@cui.unige.ch
Abstract. For our first participation in the CLEF evaluation campaign, our aim is to explore a
translation-free technique for multilingual information retrieval. This technique is based on an
ontological representation of documents and queries. We use a multilingual ontology for
documents/queries representation. For each language, we use the multilingual ontology to map a term
to its corresponding concept. The same mapping is applied to each document and each query. Then, we
use a classic vector space model for the indexing and the querying. The main advantages of our
approach are: no merging phase is required, no dependency on automatic translators between all pairs
of languages exists, and adding a new language only requires a new mapping dictionary to the
multilingual ontology.
Key words: Multilingual Ontology, Conceptual Indexing, Multilingual Information Retrieval.
Introduction
The existing approaches use either translation of all documents into a common language, either automatic
translation of the queries, or combination of both query and document translations [Chen at al. 2003]. In these cases,
we need automatic translators between all pairs of languages. If we translate queries, after receiving a result list from
each search engine, we need to use a merging procedure to provide a unique ranked result list. Moreover adding a
new language (query or document) requires as much translators as existing languages.
In our approach, we tried to “dissolve” these problems by using of a multilingual ontology. Based on this
ontology, we conducted different experiments involving multilingual test-collection. We retrieve documents written
in Dutch, English, Finnish, French, German, Italian, Spanish and Swedish, independently of the query language.
First, we tried to prove the feasibility of our approach using English when submitting queries. We also tried to prove
that our system is independent of the query language. Thus, we have used Dutch, French, and Spanish when
submitting queries,
In the next section, we describe our approach and present our official runs.
1. Ontology based Multilingual Information Retrieval
1.1 Multilingual ontology
A Multilingual ontology is defined by one ontology and a set of dictionary (one dictionary for each language). An
ontology is a formal, explicit specification of a shared conceptualisation [Gruber 1993]. It contains a set of distinct
and identified concepts C related by a set of relations R. In our approach, we only need to use the set of concepts.
Here, we present two examples of concepts extracted from our ontology:
• 8612 : a unit of length (in United States and Britain) equal to one twelfth of a foot
• 28845: the thick short innermost digit of the forelimb.
A dictionary DL is an association of ontology concepts C with a terms set TL pertaining to a language L. We
denote: DL : C TL. Indeed, the concept c is labelled by a set of terms t1,t2,..,tn in the language L. We denote
DL(c)={t1,t2, …,tn}. We also define the reciprocal relation SL : TL C by SL (t)={c∈ C | t ∈ DL(c)}. Actually, the
term t indicates the concepts c1,c2,…, cm. We also denote SL(t)={ c1,c2, …,cm}. Here, we present two examples of
associations between terms and concepts:
• DEN(28845)= {thumb}.
• SFR(pouce)= {8612, 28845 }.
Figure 1. Example of concept definition in
Figure 2. Example of term-concept association.
UNL[UNL].
To bootstrap and build dictionaries, we have used Esperanto dictionaries found on the Web (principally from
Ergane [ERG 2005]). We have also used automatic translations to complete some of them.
#concepts Add automatic
Language Stemmer
(human check) translations
English eng 5300
French fra 13000
German deu 20000 x
Dutch dut 81000 x
Italian ita 4500 x
Spanish spa 19000 x
Swedish sve 6500 x
Finnish fin 2000
Tableau 1. Description of the linguistic used resources.
1.2 Ontology based multilingual information retrieval
In our approach, for each document in the whole collection, we use the multilingual ontology to map each term to
its corresponding concept. We apply the same process on the queries.
The document d L = < t1, t2,…, tn> is a sequence of terms from the set TL of the language L. To carry out the term-
concept mapping, we apply the function SL on each term ti of the document dL: SL(t)={c∈ C | t ∈ DL(c)}. So we
obtain the conceptual representation of the document that we denote: CR(dL)= < S(t1), S(t2), …, S(tn)>. Finally,
CR(dL) is a sequence of sets of concepts.
We did not introduce any treatment for the term ambiguity. In fact, if the term is ambiguous, we replace it by all
its corresponding concepts.
Before the term mapping step, we use a “stop word list” for each language, and a dedicated stemming system. We
have used Snowball, a small string processing language designed for creating stemming algorithms in Information
Retrieval [Snow 2005].
We did not introduce any morpho-syntactic or processing (like n-grams) to break composite words in Dutch,
German, or Finnish.
For indexing and querying, we use the vector space model [Salton et al. 83].
TERMS CONCEPTS
Spanish
corpus
French
corpus
indexing
conceptual
... indexing process document
terms
Finnish
corpus terms Spanish dictionary
conceptual
French dictionary
stem ontology
index
... of
ming corpus
Finnish dictionary
Spanish
queries
quering process conceptual
French queries
queries
search
results
engine
...
Finnish
queries
Figure 3. Indexing and querying process
1.3 Official runs description
In our approach, each query Q is composed by two fields: a topic field and a body field. We denote
Q=. The content of each field depends on the runs. Each field is composed by a list of terms extracted
from the original text query.
As the queries are precise, we use the topic field to query the whole collection. As a result, we obtain a set of
documents containing the topic concepts. Then we use the body field to rank this set of documents.
Here we present an example of a query composed by a topic field (text between topic tags) and a body field (text
between all the other tags).
C182
Normandië Landing
50e Herdenkingsdag van de Landing in Normandië
Zoek verslagen over de dropping van veteranen boven Sainte-Mère-Église tijdens de viering van de 50e
herdenkingsdag van de landing in Normandië.
Ongeveer veertig veteranen sprongen tijdens de viering van de 50e herdenkingsdag van de landing in Normandië
met een parachute boven Sainte-Mère-Église, net zoals ze vijftig jaar eerder op D-day hadden gedaan. Alle informatie over het
programma of over de gebeurtenis zelf worden als relevant beschouwd.
Now we present our official runs. In the following three runs, we use English when submitting queries:
1. AUTOEN: the topic field is composed by the terms of the title of the original query (text between the
title tags). The body field is composed by the text of the original query.
2. ADJUSTEN: the topic field is composed by the modified title by adding and/or removing terms. The
adding terms are extracted from the original query text. The body field is composed by the text of the
original query.
3. FEEDBCKEN: the topic field is composed by the modified title as in ADJUSTEN. The body field is
composed by the original text query and the first relevant document (if it exists) in the first 30
documents found by the previous ADJUSTEN run.
Table 1 shows the result of each run. Of course it’s difficult to have a good result for the AUTOEN run while the
topic contains all the concepts corresponding to the query title terms. We succeeded in improving the result of
63.11% by using the adjusted topic. Finally, by using the relevance feedback, we improved the result of 24.74%.
This improvement is due to the vector space model which gives better results when the documents/queries vectors
are long.
100.0% precision. 70.0%
Precision
average
90.0% average
60.0%
80.0% runs in english
70.0% 50.0%
AUTO-EN Prec. Avg runs with same strategy
ADJUST-EN Prec. Avg
60.0% FEEBCK-EN Prec. Avg
40.0% ADJUST-French
50.0% ADJUST-English
30.0% ADJUST-Spanish
40.0%
ADJUST-Dutch
30.0% 20.0%
20.0%
Interpolate precision
10.0%
10.0% interpolate recall
0.0% 0.0%
0% 20% 40% 60% 80% 100% 120% 0% 20% 40% 60% 80% 100%
Figure 4. Comparison of the system result using Figure 5. Comparison of the system result using four languages
three strategies
In order to compare the system results using different languages when submitting queries, we carried out three
more runs: ADJUSTDU, ADJUSTFR, and ADJUSTSP. For each run, we use respectively Dutch, French, and
Spanish when submitting queries. In these runs, topic field is composed by modified title like in ADJUSTEN and
body field is composed by the original query text.
For all the four runs, we obtain almost the same mean average precision: 13.90% for ADJUSTDU, 13.47% for
ADJUSTFR, 13.80% for ADJUSTSP and 16.85 % for ADJSUTEN. Our system is not dependent of the query
language. It gives nearly the same results when submitting queries in four different languages. It’s difficult to explain
difference because the coverage and the quality of ontological dictionaries are important.
Run name Query language Type MAP
AUTO-EN English Automatic 10.33 %
ADJSUT-EN English Adjusted Topic 16.85 %
ADJUST-DU Dutch Adjusted Topic 13.90 %
ADJUST-FR French Adjusted Topic 13.47 %
ADJUST-SP Spanish Adjusted Topic 13.80 %
FEEDBCK-EN English Manual 21.02 %
Table 2. Description and Mean Average Precision (MAP) of our official multilingual runs
Conclusion
In this CLEF evaluation campaign, we evaluated a multilingual ontology-based approach for multilingual
information retrieval. We did not use any translation either for documents or for queries. We carried out a common
document/query representation based on multilingual ontology. Then, we used the vector space model for indexing
and querying. Compared with the existing approaches, our approach has several advantages. Indeed, there is no
dependency on automatic translators between all pairs of languages. When we add a new language, we only add, in
the ontology, a new mapping dictionary. Also, we do not need any merging technique to rank the list of retrieved
documents.
In this preliminary work, we tried only to prove the feasibility of our approach. We tried also to prove that our
system is independent of the query language. We still have some limits in our system because we did not introduce
any morpho-syntactic processing to break composite words in Dutch, German, or Finnish. Moreover, our ontology is
incomplete and dirty (we have imported many errors with automatic translation).
We have also used the same approach in the bi-text alignment field. We have used other language like Chinese,
Arabic and Russian [Guyot 2005].
Acknowledgments
We would like to thank the CLEF-2005 organisers for their efforts. We would also like to thank Metaread for
giving us the possibility to use the “idxvli” information retrieval system (A fast indexer for big corpora).
References
[Chen at al. 2003] Chen, A. and Gey, F. Combining query translation and document translation in cross-language
retrieval. In proceedings CLEF-2003, pp. 39.48. Trondheim.
[ERG 2005] Ergane: http//download.travlang.com/, see also http://www.majstro.com/-
[Guyot 2005] GUYOT, J. yaaa: yet another alignment algorithm - Alignement ontologique bi-texte pour un corpus
multilingue. Cahier du CUI 2005.
[Gruber 1993] Gruber, T. R. A translation Approach to Portable Ontology Specifications, Knowledge Acquisition,
5 : 199-220, 1993.
[UNL] UNL: Universal Networking Language. http://cui.unige.ch/isi/unl/ & http://www.undl.org/.
[Salton et al. 83] Salton, G. and Mcgill, M. J. Introduction to the modern Information Retrieval. McGraw-Hill (1983)
[Snow 2005] SnowBall: http://snowball.tartarus.org/.