Application of Ontology Translation

Ressler, James D.; Dean, Mike; Benson, Edward; Dorner, Eric; Morris, Chuck

doi:10.1007/978-3-540-76298-0_60

Cited by 15 publications

(17 citation statements)

References 6 publications

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“…Snoogle [21] is an ontology translation tool that enables the use of SWRL rules to express translations and alignments between geospatial ontologies. While in both approaches it is possible to use custom plug-ins, the user has to write functions using Java and the Jena framework.…”

Section: Related Workmentioning

confidence: 99%

Model Driven Specification of Ontology Translations

Parreiras

Staab

Schenk

et al. 2008

Lecture Notes in Computer Science

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Abstract. The alignment of different ontologies requires the specification, representation and execution of translation rules. The rules need to integrate translations at the lexical, the syntactic and the semantic layer requiring semantic reasoning as well as low-level specification of adhoc conversions of data. Existing formalisms for representing translation rules cannot cover the representation needs of these three layers in one model. We propose a metamodel-based representation of ontology alignments that integrate semantic translations using description logics and lower level translation specifications into one model of representation for ontology alignments.

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Section: Related Workmentioning

confidence: 99%

Model Driven Specification of Ontology Translations

Parreiras

Staab

Schenk

et al. 2008

Lecture Notes in Computer Science

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“…There exist a plethora of tools for defining alignments between RDF models that can be found in the literature. Some of the most remarkable are COMA++ [18], Sambo [19], Snoggle [20], Optima [21], Ontostudio [22], AgreementMaker [23] and Bio-Mixer [24]. These tools provide different features and capabilities, and resort to different data structures for representing the aligned RDF schemas -e.g.…”

Section: Discussionmentioning

confidence: 99%

Toward a View-oriented Approach for Aligning RDF-based Biomedical Repositories

Anguita

García-Remesal²,

Iglesia³

et al. 2015

Methods Inf Med

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The importance of database integration methods and tools in the context of biomedical research has been widely recognized. Modern research in this area - e.g. identification of disease biomarkers, or design of personalized therapies - heavily relies on the availability of a technical framework to enable researchers to uniformly access disparate repositories. We present a method and a tool that implement a novel alignment method specifically designed to support and enhance the integration of RDF-based data sources at schema (metadata) level. This approach provides an increased level of expressiveness compared to other existing solutions, and allows solving heterogeneity scenarios that cannot be properly represented using other state-of-the-art techniques.

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“…Language databases are in fact huge ontologies that represent the lexical and the structural characteristic of the languages (e.g., WordNet [19] for English language). Computer-aided translation of different languages has been made possible by the alignment of their respective ontologies [20] . Word sense disambiguation [21,22] , which is the problem of finding the true sense of the word within the context in either monolingual or multilingual text has been solved using the ontology alignment techniques.…”

Section: Introductionmentioning

confidence: 99%

Multimodal Integration (Image and Text) Using Ontology Alignment

Abu-Shareha¹,

Rajeswari²,

Ramachandram³

2009

American J. of Applied Sciences

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Problem statement: This study proposed multimodal integration method at the concept level to investigate information from multimodalities. The multimodal data was represented as two separate lists of concepts which were extracted from images and its related text. The concepts extracted from image analysis are often ambiguous, while the concepts extracted from text processing could be sense-ambiguous. The major problems that face the integration of the underlying modalities (image and text) were: The difference in the coverage and the difference in the granularity level. Approach: This study proposed a novel application using ontology alignment to unify the underlying ontologies. The said lists of concepts were represented in a structured form within the corresponding ontologies then the two structural lists are enriched and matched based on the alignment, this matching represent the final knowledge. Results: The difference in the coverage was solved in this study using the alignment process and the difference in the granularity level was solved using the enrichment process. Thus, the proposed integration produced accurate integrated results. Conclusion: Thus, integration of these concepts allows the totality of the knowledge be expressed more precisely.

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Application of Ontology Translation

Cited by 15 publications

References 6 publications

Model Driven Specification of Ontology Translations

Model Driven Specification of Ontology Translations

Toward a View-oriented Approach for Aligning RDF-based Biomedical Repositories

Multimodal Integration (Image and Text) Using Ontology Alignment

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