A configurable translation-based cross-lingual ontology mapping system to adjust mapping outcomes

Fu, Bo; Brennan, Rob; O’Sullivan, Declan

doi:10.1016/j.websem.2012.06.001

Cited by 23 publications

(15 citation statements)

References 29 publications

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“…In the translation step, they depend on getting one translation for each concept (one-to-one translation), then they apply monolingual matching approaches to match concepts between the source ontologies and the translated ones. Fu et al [10,11] proposed an approach to match English and Chinese ontologies by considering the semantics of the target ontology, the mapping intent, the operating domain, the time and resource constraints and user feedback. Hertling and Paulheim [13] proposed an approach which utilizes Wikipedia's inter-language links for finding corresponding ontology elements.…”

Section: Related Workmentioning

confidence: 99%

“…We identified four of the related approaches (AML, KEPLER, LogMap, and XMap) to be included in our evaluation in addition to OECM 1.0. The other related work, neither publish their code, nor their evaluation datasets [10,11,25]. In order to compare our results with the state-of-the-art, we use German (Conference de ) and Arabic (Conference ar ) versions of the Conference ontology as the source ontologies, and Ekaw en and Edas en ontologies as the target English ontologies.…”

Section: Effectiveness Of Oecmmentioning

confidence: 99%

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From Monolingual to Multilingual Ontologies: The Role of Cross-Lingual Ontology Enrichment

Ibrahim

Fathalla

Yazdi

et al. 2019

Lecture Notes in Computer Science

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While the multilingual data on the Semantic Web grows rapidly, the building of multilingual ontologies from monolingual ones is still cumbersome and hampered due to the lack of techniques for crosslingual ontology enrichment. Cross-lingual ontology enrichment greatly facilitates the semantic interoperability between different ontologies in different natural languages. Achieving such enrichment by human labor is a time-consuming and error-prone task. Thus, in this paper, we propose a fully automated ontology enrichment approach using cross-lingual matching (OECM) approach, which builds a multilingual ontology by enriching a monolingual ontology from another one in a different natural language using a cross-lingual matching. OECM selects the best translation among all available translations of ontology concepts based on their semantic similarity with the target ontology concepts. We present a use case of our approach for enriching English Scholarly Communication Ontologies using German and Arabic ontologies from the Multi-Farm benchmark. We have compared our results with the results from the Ontology Alignment Evaluation Initiative (OAEI 2018). Our approach has higher precision and recall in comparison to five state-of-theart approaches. Additionally, we recommend some linguistic corrections in the Arabic ontologies in Multifarm which have enhanced our crosslingual matching results.

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Section: Related Workmentioning

confidence: 99%

Section: Effectiveness Of Oecmmentioning

confidence: 99%

From Monolingual to Multilingual Ontologies: The Role of Cross-Lingual Ontology Enrichment

Ibrahim

Fathalla

Yazdi

et al. 2019

Lecture Notes in Computer Science

View full text Add to dashboard Cite

show abstract

“…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.…”

Section: Ontology Mappingmentioning

confidence: 99%

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

Kuai

Lin

Luo

et al. 2016

IJGI

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Abstract:The need for integrating geospatial information (GI) data from various heterogeneous sources has seen increased importance for geographic information system (GIS) interoperability. Using domain ontologies to clarify and integrate the semantics of data is considered as a crucial step for successful semantic integration in the GI domain. Nevertheless, mechanisms are still needed to facilitate semantic mapping between GI ontologies described in different natural languages. This research establishes a formal ontology model for cross-lingual geospatial information ontology mapping. By first extracting semantic primitives from a free-text definition of categories in two GI classification standards with different natural languages, an ontology-driven approach is used, and a formal ontology model is established to formally represent these semantic primitives into semantic statements, in which the spatial-related properties and relations are considered as crucial statements for the representation and identification of the semantics of the GI categories. Then, an algorithm is proposed to compare these semantic statements in a cross-lingual environment. We further design a similarity calculation algorithm based on the proposed formal ontology model to distance the semantic similarities and identify the mapping relationships between categories. In particular, we work with two GI classification standards for Chinese and American topographic maps. The experimental results demonstrate the feasibility and reliability of the proposed model for cross-lingual geospatial information ontology mapping.

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“…Despite the good quality of mappings generated by this approach, it cannot be used to process large and complex ontologies. Therefore, researchers have turned to automated approaches using different techniques: machine learning [21], machine translation [22], extraction mappings using multilingual background [23], etc. Overall, the ontology alignment community mostly focuses on the topic of generating mappings between different ontologies in different languages [19,24] and ignores the problem of mapping reconciliation considered (truly) as a more easy issue.…”

Section: Related Workmentioning

confidence: 99%

Multilingual Mapping Reconciliation between English-French Biomedical Ontologies

Annane

Emonet

Azouaou

et al. 2016

Proceedings of the 6th International Conference on Web Intelligence, Mining and Semantics

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Even if multilingual ontologies are now more common, for historical reasons, in the biomedical domain, many ontologies or terminologies have been translated from one natural language to another resulting in two potentially aligned ontologies but with their own specificity (e.g., format, developers, and versions). Most often, there is no formal representation of the translation links between translated ontologies and original ones and those mappings are not formally available as linked data. However, these mappings are very important for the interoperability and the integration of multilingual biomedical data. In this paper, we propose an approach to represent translation mappings between ontologies based on the NCBO BioPortal format. We have reconciled more than 228K mappings between ten English ontologies hosted on NCBO BioPortal and their French translations. Then, we have stored both the translated ontologies and mappings on a French customized version of the platform, called the SIFR BioPortal, making the whole thing available in RDF. Reconciling the mappings turned more complex than expected because the translations are rarely exactly the same than the original ontologies as discussed in this paper.

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A configurable translation-based cross-lingual ontology mapping system to adjust mapping outcomes

Cited by 23 publications

References 29 publications

From Monolingual to Multilingual Ontologies: The Role of Cross-Lingual Ontology Enrichment

From Monolingual to Multilingual Ontologies: The Role of Cross-Lingual Ontology Enrichment

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

Multilingual Mapping Reconciliation between English-French Biomedical Ontologies

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