Exploring Rank Aggregation for Cross-Lingual Ontology Alignments: A Comparative Study

Tracking #: 2679-3893

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Juliana Medeiros Destro
Javier Alvaro Vargas Muñoz
Julio Cesar dos Reis
Ricardo da Silva Torres1

Responsible editor: 
Philipp Cimiano

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Full Paper
Cross-language ontology alignments are of paramount importance in several applications. A common approach to define proper alignments relies on identifying the relationships among concepts from different ontologies by performing multiple entity-based searches. In this strategy, the most suitable matching is defined by the top-ranked concept found. Often, multiple similarity rankers, defined in terms of different similarity criteria, are considered to define candidate entities. In this case, their complementary view can be exploited in the definition of the best possible matching. In this paper, we explore the use of rank aggregation functions, under both unsupervised and supervised settings, in the task of defining suitable matches among entities belonging to ontologies encoded in distinct languages. We conducted a comparative study involving a comprehensive set of experiments with standard datasets from the OAEI competition, using ontologies in the Conference domain, and mappings among 36 language pairs. Experimental results show that supervised rank aggregation approaches, particularly LambdaMART and Random Forest, leads to better results in cross-language ontology matching when compared with unsupervised techniques.
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