Abstract
Ontology matching in a multilingual environment consists of finding alignments between ontologies modeled by using more than one language. Such a research topic combines traditional ontology matching algorithms with the use of multilingual resources, services, and capabilities for easing multilingual matching. In this paper, we present a multilingual ontology matching approach based on Information Retrieval (IR) techniques: ontologies are indexed through an inverted index algorithm and candidate matches are found by querying such indexes. We also exploit the hierarchical structure of the ontologies by adopting the PageRank algorithm for our system. The approaches have been evaluated using a set of domain-specific ontologies belonging to the agricultural and medical domain. We compare our results with existing systems following an evaluation strategy closely resembling a recommendation scenario. The version of our system using PageRank showed an increase in performance in our evaluations.
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Notes
- 1.
The list of supported languages is available in the Lucene analyser documentation.
- 2.
We used the text processors included in the Lucene (http://lucene.apache.org) library. In case of unavailability of libraries for a particular language, the original label is indexed as it is without being processed.
- 3.
The ontologies, their indexed version, and gold standard can be found at https://db.tt/p959AWhO.
- 4.
The 2012 version of the YAM++ system yields the same results as the 2013 version.
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Acknowledgements
The Know-Center is funded within the Austrian COMET Program under the auspices of the Austrian Ministry of Transport, Innovation and Technology, the Austrian Ministry of Economics and Labour and by the State of Styria. COMET is managed by the Austrian Research Promotion Agency FFG.
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Rexha, A., Dragoni, M., Kern, R., Kröll, M. (2016). An Information Retrieval Based Approach for Multilingual Ontology Matching. In: Métais, E., Meziane, F., Saraee, M., Sugumaran, V., Vadera, S. (eds) Natural Language Processing and Information Systems. NLDB 2016. Lecture Notes in Computer Science(), vol 9612. Springer, Cham. https://doi.org/10.1007/978-3-319-41754-7_45
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