Download Database and Expert Systems Applications: 21st International by Zeynep Pehlivan, Myriam Ben-Saad, Stéphane Gançarski PDF

By Zeynep Pehlivan, Myriam Ben-Saad, Stéphane Gançarski (auth.), Pablo García Bringas, Abdelkader Hameurlain, Gerald Quirchmayr (eds.)

st We welcome you to the lawsuits of the 21 overseas convention on Database and specialist structures purposes held in Bilbao. With info and database s- tems being a crucial subject of desktop technological know-how, it used to be to be anticipated that the integ- tion of information, details and information is this day contributing to the back swiftly expanding popularity of this box for researchers and practitioners. because its starting place in 1990, DEXA has been an annual foreign convention, positioned in Europe, which showcases state of the art learn actions in those parts. DEXA 2010 persevered this practice and supplied a discussion board for offering and discu- ing learn ends up in the realm of database and clever structures and complex - seek issues, purposes and essentially proper concerns concerning those parts. It provided attendees the chance to broadly talk about requisites, difficulties, and suggestions within the box within the friendly surroundings of the town of Bilbao, that is recognized for its riding industriousness, its most sensible cultural venues and its wealthy and encouraging he- tage and way of life. The college of Deusto with its nice academic and learn traditions, and the hospitality which the collage and the town are so recognized for, set the level for this year’s DEXA convention. This quantity includes the papers chosen for presentation on the DEXA conference.

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Additional info for Database and Expert Systems Applications: 21st International Conference, DEXA 2010, Bilbao, Spain, August 30 - September 3, 2010, Proceedings, Part I

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Zhang et al. 12. : Automatic extraction of ontologies wrapping relational data sources. , Wagner, R. ) DEXA 2009. LNCS, vol. 5690, pp. 128–142. Springer, Heidelberg (2009) 13. : Extending object-oriented databases for fuzzy information modeling. Information Systems 29(5), 421–435 (2004) 14. : A Fuzzy Ontology Generation Framework from Fuzzy Relational Databases. Int. J. Semantic Web Information Systems 4(3), 1–15 (2008) 15. : Ontology Learning for the Semantic Web. IEEE Intelligent Systems 16(2), 72–79 (2001) 16.

Given a FOOD model FS = (FCFS, FAFS, FDFS) in Definition 2. The fuzzy OWL DL ontology structure FOS = ϕ(FS) = (FID0, FAxiom0) can be derived by function ϕ as shown in Table 2. Table 2. Translating rules from a FOOD model to a fuzzy OWL DL ontology structure FOOD model FS = (FCFS, FAFS, FDFS) fuzzy class FCFS, fuzzy attribute FAFS Each fuzzy class symbol FC Each type expression Record FA : Set-of FT End Each object attribute symbol FA Each object attribute symbol FA Fuzzy ontology structure FOS = ϕ(FS) = (FID0, FAxiom0) identifier set FID0 A fuzzy class identifier ϕ(FC) ∈ FCID0 A fuzzy class identifier ϕ(FA) ∈ FCID0 Add two additional roles FU1 and FU2 ; FU1 and FU2 are mapped into two fuzzy object property identifiers ϕ(FU1) and ϕ(FU2): ϕ(FU1) ∈ FOPID0 ϕ(FU2) ∈ FOPID0 ; In addition, we use symbols FV1 and FV2 denote the inverse properties of ϕ(FU1) and ϕ(FU2), respectively: FV1 = invof_ϕ(FU1) ∈ FOPID0 FV2 = invof_ϕ(FU2) ∈ FOPID0 Each type expression Record FA1:FD1 ,…, FAk:FDk End Each datatype attribute symbol FAi Each domain symbol FDi A fuzzy datatype property identifier ϕ(FAi) ∈ FDPID0 A fuzzy XML Schema datatype identifier ϕ(FDi) ∈ FDRID0 fuzzy class declarations FDFS fuzzy class/property axiom set FAxiom0 Each fuzzy class declaration: Class FC is-a FC1 ,…, FCn Create a fuzzy class axiom: Class ( ϕ(FC) partial ϕ(FC1), …, ϕ(FCn) ).

246–250 (1997) 30 F. Zhang et al. 12. : Automatic extraction of ontologies wrapping relational data sources. , Wagner, R. ) DEXA 2009. LNCS, vol. 5690, pp. 128–142. Springer, Heidelberg (2009) 13. : Extending object-oriented databases for fuzzy information modeling. Information Systems 29(5), 421–435 (2004) 14. : A Fuzzy Ontology Generation Framework from Fuzzy Relational Databases. Int. J. Semantic Web Information Systems 4(3), 1–15 (2008) 15. : Ontology Learning for the Semantic Web. IEEE Intelligent Systems 16(2), 72–79 (2001) 16.

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