Multilingual Ontology Merging Using Cross-lingual Matching

Ibrahim, Shimaa; Fathalla, Said; Lehmann, Jens; Jabeen, Hajira

doi:10.1109/wiiat50758.2020.00020

Cited by 4 publications

(4 citation statements)

References 18 publications

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“…All translations produced by Google Translate for each class are considered during the matching process. They proposed another approach for merging monolingual ontologies in various natural languages to produce a multilingual ontology [11]. First, the alignments between input ontologies are identified using the cross-lingual matching techniques, then adds them to the merged multilingual ontology by adding rdfs:label for each language (using language-tagged strings).…”

Section: A Cross-lingual Matching Approachesmentioning

confidence: 99%

“…We match monolingual ontologies from BioPortal 10 -a web-based application for accessing and sharing biomedical ontologies and providing alignments between them. We match the Sample Processing and Separation Techniques Ontology (SEP) 11 with several ontologies from the Bio-portal such as Plant Experimental Conditions (PECO), Plant (PO), 13 Plant Trait (PTO), 14 and Units of Measurement (UO) 15 ontologies. Table 5 shows the statistics of the Bioportal ontologies and the degree of overlap between SEP with each ontology.…”

Section: B Use Case 2: Monolingual Matching In Biomedical Domainmentioning

confidence: 99%

“…As a result, determining what quality metrics that affect the quality of the ontology matching process is complex. In our previous work MULON [11], an approach for merging monolingual ontologies in different natural languages, we proposed seven quality metrics to assess the quality of ontologies on the schema level. In this paper, we extend the quality metrics, by proposing the Quality Assessment Suite for Ontologies (QASO).…”

Section: Introductionmentioning

confidence: 99%

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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%

Section: B Use Case 2: Monolingual Matching In Biomedical Domainmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Toward the Multilingual Semantic Web: Multilingual Ontology Matching and Assessment

et al. 2023

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“…yes see Tables 8 and 9 WordsAPI A Web API for (English) word definitions, multiple word relations, and more. [137] used in the past are still available; therefore, column Resource Available indicates whether the resource is still available to researchers. Due to the frequent usage of WordNet [92], systems that use this source are listed in Tables 8 and 9 which are organized according to the same methodology as Tables 4 to 7.…”

Section: Yesmentioning

confidence: 99%

Background knowledge in ontology matching: A survey

Portisch

Hladik

Paulheim

2022

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Ontology matching is an integral part for establishing semantic interoperability. One of the main challenges within the ontology matching operation is semantic heterogeneity, i.e. modeling differences between the two ontologies that are to be integrated. The semantics within most ontologies or schemas are, however, typically incomplete because they are designed within a certain context which is not explicitly modeled. Therefore, external background knowledge plays a major role in the task of (semi-) automated ontology and schema matching. In this survey, we introduce the reader to the general ontology matching problem. We review the background knowledge sources as well as the approaches applied to make use of external knowledge. Our survey covers all ontology matching systems that have been presented within the years 2004–2021 at a well-known ontology matching competition together with systematically selected publications in the research field. We present a classification system for external background knowledge, concept linking strategies, as well as for background knowledge exploitation approaches. We provide extensive examples and classify all ontology matching systems under review in a resource/strategy matrix obtained by coalescing the two classification systems. Lastly, we outline interesting and yet underexplored research directions of applying external knowledge within the ontology matching process.

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An Adaptive High Reasoning Educational Interpretable Evaluation Model for Children’s Literature Based on an Intelligent Library System

Liu

2023

Applied Artificial Intelligence

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Multilingual Ontology Merging Using Cross-lingual Matching

Cited by 4 publications

References 18 publications

Toward the Multilingual Semantic Web: Multilingual Ontology Matching and Assessment

Toward the Multilingual Semantic Web: Multilingual Ontology Matching and Assessment

Background knowledge in ontology matching: A survey

An Adaptive High Reasoning Educational Interpretable Evaluation Model for Children’s Literature Based on an Intelligent Library System

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