IOTA: Interlinking of heterogeneous multilingual open fiscal DaTA

Musyaffa, Fathoni A.; Vidal, María-Esther; Orlandi, Fabrizio; Lehmann, Jens; Jabeen, Hajira

doi:10.1016/j.eswa.2019.113135

Cited by 5 publications

(2 citation statements)

References 10 publications

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“…Four language versions of WordNet (Arabic, Italian, Slovene, and Spanish) are mapped to the English WordNet. Musyaffa et al [25] proposed a framework for interlinking heterogeneous multilingual open fiscal data. Machine translation and similarity measures are used to map concepts across different languages.…”

Section: A Cross-lingual Matching Approachesmentioning

confidence: 99%

Toward the Multilingual Semantic Web: Multilingual Ontology Matching and Assessment

et al. 2023

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The amount of multilingual data on the Web proliferates; therefore, developing ontologies in various natural languages is attracting considerable attention. In order to achieve semantic interoperability for the multilingual Web, cross-lingual ontology matching techniques are highly required. This paper proposes a Multilingual Ontology Matching (MoMatch) approach for matching ontologies in different natural languages. MoMatch uses machine translation and various string similarity techniques to identify correspondences across different ontologies. Furthermore, we propose a Quality Assessment Suite for Ontologies (QASO) that comprises 14 metrics, out of which seven metrics are used to assess the quality of the matching process and seven metrics are used to evaluate the quality of the ontology. We present an indepth comparison of different string similarity techniques across various languages to get the most effective similarity measure(s) between multilingual terms. To illustrate the applicability of our approach and how it can be used in different domains, we present two use cases. MoMatch has been implemented using Scala and Apache Spark under an open-source license. We have compared our results with the results from the Ontology Alignment Evaluation Initiative (OAEI 2020). MoMatch has achieved significantly high precision, recall, and F-measure compared to five state-of-the-art matching approaches.

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Section: A Cross-lingual Matching Approachesmentioning

confidence: 99%

Toward the Multilingual Semantic Web: Multilingual Ontology Matching and Assessment

et al. 2023

Self Cite

View full text Add to dashboard Cite

show abstract

“…However, certain crucial disadvantages will endure for years. First, it requires a sufficient amount of domain knowledge in the schemas of all relevant datasets, as well as a number of acceptable ontology vocabularies for semantic enrichment [58,59]. Second, it will significantly increase the complexity of the data as the ontologies created by various parties are challenged to be united in compliance with some common protocol.…”

Section: Interlinking Diverse Datamentioning

confidence: 99%