=Paper=
{{Paper
|id=Vol-3324/om2022_poster4
|storemode=property
|title=Multifarm11 - extending the multifarm benchmark for Hindi language
|pdfUrl=https://ceur-ws.org/Vol-3324/om2022_poster4.pdf
|volume=Vol-3324
|authors=Abhisek Sharma,Sarika Jain,Cรกssia Trojahn
|dblpUrl=https://dblp.org/rec/conf/semweb/Sharma0T22
}}
==Multifarm11 - extending the multifarm benchmark for Hindi language==
https://ceur-ws.org/Vol-3324/om2022_poster4.pdf
๐๐ข๐๐ก๐๐ ๐๐๐11 - Extending the Multifarm Benchmark for
Hindi Language
Abhisek Sharma1,โ , Sarika Jain1 and Cassia Trojahn2
1
National Institute of Technology Kurukshetra, India
2
Toulouse University (IRIT), France
Abstract
Multifarm is a well-known comprehensive dataset for multilingual ontology matching evaluation.We
extend the Multifarm dataset in the eleventh language, i.e., Hindi (๐๐ข๐๐ก๐๐ ๐๐๐11 ). This Hindi compo-
nent of ๐๐ข๐๐ก๐๐ ๐๐๐11 1 has been created by translating the entities using the Google translation service,
validating manually, and then creating the reference alignments for the matching task. Work is in
progress to determine the impact of ๐๐ข๐๐ก๐๐ ๐๐๐11 on different multilingual ontology alignment systems.
The complexities introduced by the Hindi language will introduce novel challenges to the behavior of
cross-lingual ontology matching systems.
Keywords
Multifarm Benchmark, Hindi Dataset, Ontology Matching, Reference Alignment, Multilingual.
1. Introduction and Motivation
Multifarm [1] has been the commonly accepted benchmark dataset for Multilingual Ontology
Matching since 2011 created on the basis of the Ontofarm dataset from the OAEI campaigns.
It consists of seven ontologies of the conference domain. During its first inception, the Mul-
tifarm track was available in eight different languages other that English โ Chinese, Czech,
Dutch, French, German, Portuguese, Russian, and Spanish; later it was extended to include
Arabic language in 2015 [2]. Introducing more languages that introduces more challenges and
increases the scope of improvement of multilingual ontology matching systems is always ben-
eficial.
Hindi is the fourth most spoken language in the world and is an indispensable part of the In-
dian identity and culture. It has its root in the mother of all languages, Sanskrit and Prakrit
and is written using the Devanagari script. Hindi is known for its free order, multiple variants,
and ambiguity in senses. The Hindi language offers different representation in both lexical and
contextual sense and suffers from resource unavailability. All these mentioned aspects makes
Hindi a worthy addition to the list of languages Multifarm is available in.
We present a proof of concept to extend the Multifarm dataset in the eleventh language, i.e.,
1
Dataset archive can be found here - https://bit.ly/3QxkHRu
โ
Corresponding author.
ยฃ abhisek_61900048@nitkkr.ac.in (A. Sharma); jasarika@nitkkr.ac.in (S. Jain); cassia.trojahn@irit.fr (C. Trojahn)
ศ 0000-0003-1568-2625 (A. Sharma); 0000-0002-7432-8506 (S. Jain); 0000-0003-2840-005X (C. Trojahn)
ยฉ 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
Workshop
Proceedings
http://ceur-ws.org
ISSN 1613-0073
CEUR Workshop Proceedings (CEUR-WS.org)
Hindi with an aim to include the same during the OAEI ontology matching workshops. After
Chinese, Hindi has more characters than any other available language in the Multifarm track.
For any Computer system to take contextually correct decisions and serve the vast population
that speaks Hindi, we need more datasets in it that are helping one or more type of computer
operations (in this case, Ontology matching). Many letters (and even words) in English has vari-
ous mappings in Hindi. For example, โSโ can be written as เคถ, เคท or เคธ. Hindi has no capitalization,
though have short vowels. Version and variation of word in Hindi is context dependent. Same
word in English (or any other languages) can have different Hindi associated word, depending
on the context.
2. The ๐๐ข๐๐ก๐๐ ๐๐๐11 Benchmark
The Hindi Language Component is developed referring to the multifarm dataset of the OAEI
campaign. The structure of the ontologies and reference alignments has been reused while
enriching them with the contextually verified entities in Hindi language. After including Hindi,
a total of 55 language pairs will be there for the evaluation of the matching systems.
1. Translation of Ontology Entities - A total of around 2500 terms were fetched from
the seven ontologies of the dataset and enlisted, out of which around 1000 are unique.
Translations of the entities were done with the Google translation service.
2. Validation - Contextual verification was performed in line with conference domain. Er-
rors like โpaperโ, which was translated to โเคเคพเคเฅโ, which was contextually corrected to
โเคถเฅเคง เคชเคคเฅเคฐ. The task requires validators to have knowledge of conference domain and Hindi
Language. The authors are well suited for the task, they all are aware of conference do-
main as they all are researchers and for Hindi, two of them are native Hindi speakers.
3. Generation of Reference Alignments - The reference alignments were created by
reproducing the alignments based on the reference alignments available in the multifarm
track. For example, dokument (of cmt ontology) in German is aligned to เคธเคฎเฅเคฎเฅ เคฒเคจ เคฆเคธเฅเคคเคพเคตเฅ เฅ
in Hindi (of conference ontology).
Acknowledgements
This work is supported by the IHUB-ANUBHUTI-IIITD FOUNDATION set up under the NM-
ICPS scheme of the Department of Science and Technology, India
References
[1] Meilicke, C., Garcia-Castro, R., Freitas, F., Van Hage, W.R., Montiel-Ponsoda, E., De
Azevedo, R.R., Stuckenschmidt, H., ล vรกb-Zamazal, O., Svรกtek, V., Tamilin, A. and Trojahn,
C., 2012. MultiFarm: A benchmark for multilingual ontology matching. Journal of web
semantics, 15, pp.62-68.
[2] Khiat, A., Benaissa, M. and Jimรฉnez-Ruiz, E., 2015. ADOM: arabic dataset for evaluating
arabic and cross-lingual ontology alignment systems. OM, 1545, pp.50-54.