2002
DOI: 10.1007/3-540-45810-7_23
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MAFRA — A MApping FRAmework for Distributed Ontologies

Abstract: Abstract. Ontologies as means for conceptualizing and structuring domain knowledge within a community of interest are seen as a key to realize the Semantic Web vision. However, the decentralized nature of the Web makes achieving this consensus across communities difficult, thus, hampering efficient knowledge sharing between them. In order to balance the autonomy of each community with the need for interoperability, mapping mechanisms between distributed ontologies in the Semantic Web are required. In this pape… Show more

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Cited by 227 publications
(172 citation statements)
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“…Among works covering lexical, syntactic and semantic translations, MAFRA [8] and RDFT [9] are frameworks enabling dataset translations. Nonetheless, both are based on RDF Schema and neither they provide the expressiveness of OWL-DL nor support reasoning capabilities of DL inference engines.…”
Section: Related Workmentioning
confidence: 99%
See 1 more Smart Citation
“…Among works covering lexical, syntactic and semantic translations, MAFRA [8] and RDFT [9] are frameworks enabling dataset translations. Nonetheless, both are based on RDF Schema and neither they provide the expressiveness of OWL-DL nor support reasoning capabilities of DL inference engines.…”
Section: Related Workmentioning
confidence: 99%
“…For addressing ontology translation problems at lexical and syntactic layers, alignment frameworks take advantage of platform-specific implementations, sometimes abstracted into translation patterns [8] [9] or into logical built-ins [7].…”
Section: Introductionmentioning
confidence: 99%
“…For this reason, great attention is being devoted to the possibility of creating mappings in an automatic or semi-automatic way (Vizine-Goetz, Hickey, Houghton, Thompsen, 2004), , (Kalfoglou & Schorlemmer, 2003) and (Maedche, Motik, Silva, Volz, 2002). However, so far, research has focused mainly on the quantitative analysis of the automatically obtained mappings, i.e.…”
Section: Introductionmentioning
confidence: 99%
“…These tools also show promising applications in the database arena (see [26] for a survey). Chimaera [22], Protégé [27], GLUE [9] and MAFRA [21] are some examples of such systems. GLUE [9] employs machine learning and exploits data instances to find matchings between concepts.…”
Section: Related Workmentioning
confidence: 99%
“…This system updates its suggestions based on the input of user and gives new alignments to user. MAFRA [21] is an interactive, incremental and dynamic framework, which builds a semantic bridging ontology for distributed ontologies. These semantic bridges specify how to translate entities from source ontology to target ontology.…”
Section: Related Workmentioning
confidence: 99%