MultiFarm: A benchmark for multilingual ontology matching

Abstract

a b s t r a c tIn this paper we present the MultiFarm dataset, which has been designed as a benchmark for multilingual ontology matching. The MultiFarm dataset is composed of a set of ontologies translated in different languages and the corresponding alignments between these ontologies. It is based on the OntoFarm dataset, which has been used successfully for several years in the Ontology Alignment Evaluation Initiative (OAEI). By translating the ontologies of the OntoFarm dataset into eight different languages -Chinese, Czech, Dutch, French, German, Portuguese, Russian, and Spanish -we created a comprehensive set of realistic test cases. Based on these test cases, it is possible to evaluate and compare the performance of matching approaches with a special focus on multilingualism.

Geospatial Information Categories Mapping in a Cross-lingual Environment: A Case Study of “Surface Water” Categories in Chinese and American Topographic Maps

“…Author in [39] groups the existing cross lingual ontology mapping(CLOM) algorithms into following categories: manual processing [40][41][42], corpus-based approach [43], linguistic enrichment [44], indirect alignment composition [45], and translation-based approach [39,46]. Compared to these CLOM approaches, translation-based CLOM is currently a very popular approach that is exercised by several researchers [47][48][49][50], which is enabled by translations achieved through the use of machine translation (MT) tools, bilingual/multilingual thesauri, dictionaries etc.…”

Towards the Multilingual Semantic Web

“…While systematic evaluation of monolingual ontology matching systems has been carried out extensively in the context of the Ontology Alignment Evaluation Initiative (OAEI) campaigns (Euzenat et al 2011), evaluation of cross-lingual and multilingual matching systems has not received much attention until more recently (Aguirre et al 2012;Meilicke et al 2012). While systematic evaluation of monolingual ontology matching systems has been carried out extensively in the context of the Ontology Alignment Evaluation Initiative (OAEI) campaigns (Euzenat et al 2011), evaluation of cross-lingual and multilingual matching systems has not received much attention until more recently (Aguirre et al 2012;Meilicke et al 2012).…”

“…The lack of benchmarks for automatic evaluation of crosslingual 14 matching systems has motivated the creation of the MultiFarm data set(Meilicke et al 2012). The FAO data set is about matching an SKOS version of part of the United Nations Food and Agriculture Organization (FAO) AGROVOC multilingual thesaurus with the United States National Agricultural Library (NAL) Agricultural thesaurus (monolingual).…”