Semantic Web-Based Information Systems: State-of-the-Art ...

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Figure 6. An example SimilarTo pattern Web Serv ce Messages SimilarTo_1 (and (rdf:type ?x http://www.sample.org/openEHRweight-birth#Body_weight_at_birth) (state ?x ?y)) ... among the classes and properties of the source and target ontology. The patterns are also specified as OWL class instances in the Mapping Specification. As an example, SimilarTo pattern is shown in Figure 6. However, the use of OWL as a mapping definition language has some shortcomings, as stated in Brujin and Polleres (2004). One of the shortcomings of using OWL as a mapping definition language is its tight coupling between the source and the target ontologies. A mapping definition needs to import other related (source and target) ontologies with owl:import. This results in a tight coupling between ontolo- Figure 7. Mapping object properties Copyright © 2007, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited.
B cer, K l c, Dogac, & Lalec Figure 8. An example JavaScript function copy_code(openEHR_code) { if(openEHR_code.equals("Naked")) return "8351-9"; else if(openEHR_code.equals(Dressed") return "8350-1"; } gies, which is undesirable, because it makes one ontology dependent on another in the sense that axioms and definitions in one ontology use classes and properties from the other ontology. This can result in such things as the necessity to use the other externally specified ontology in order to perform certain local reasoning tasks (Brujin & Polleres, 2004). Therefore, rather than using pure OWL, we specify queries in our mapping definition in OWL-QL KIF syntax which are then executed by the mapping engine. Furthermore, the value transformations also should be expressed in the Mapping Definition. We specify the value transformations as JavaScript strings of the DatatypePropertyTransform pattern. Transforming.the.Archetype.Instances Consider the example presented in Figure 2. Hospital B, using the archetype instances based on HL7, wishes to invoke the Web service provided by Hospital A in order to receive the BirthWeight information for a patient. Through the Web service Invocation Interface provided by the ARTEMIS peer, Hospital B provides the Web service input as PatientInfo archetype instance based on HL7 RIM and wishes to receive the result as a BirthWeight archetype instance again based on HL7 RIM. Note that Hospital A has declared to the mediator that its Web service exchanges are based on OpenEHR archetypes. When the mediator invokes this Web service on behalf of Hospital B, the invocation tool in the mediator consults to the OWLmt Mapping Engine for transforming the archetype instances from source ontology to the target ontology. In this section, we detail how this instance transformation is achieved through the OWLmt mapping engine. The OWLmt mapping engine creates the target archetype instances in OWL, using the mapping patterns in the Mapping Definition and the instances of the source archetype. It uses OWL Query Language (OWL-QL) (Fikes, Hayes, & Horrocks, 2003) to retrieve required data from the source ontology instances. While executing the Copyright © 2007, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited.
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Semantic Web-Based Information Syst
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Semantic Web-Based Information Syst
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Chapter.VIII Querying.the.Web.Recon
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the semantics in the Semantic Web.
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the conventional Web, and the emerg
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Chapter.I Semant cs for the Semant
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Semant cs for the Semant c Web and
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Semant cs for the Semant c Web •
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Semant cs for the Semant c Web reas
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Semant cs for the Semant c Web simi
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Semant cs for the Semant c Web in t
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Entity Identification/Disambiguatio
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Question Answering Systems Semant c
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Semant cs for the Semant c Web The
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Semant cs for the Semant c Web Dubo
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Semant cs for the Semant c Web Shet
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The Human Semant c Web tion between
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The Human Semant c Web Figure 2. Th
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• Databases with entry-portals
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The Human Semant c Web • Knowledg
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Conceptual.Modeling The Human Seman
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The Human Semant c Web in relations
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The Human Semant c Web along the di
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The Human Semant c Web Figure 7. Th
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The Human Semant c Web Figure 10. T
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Conceptual.Calibration The Human Se
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The Human Semant c Web Figure 12. T
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The Human Semant c Web Figure 14. S
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Figure 16. The present structure of
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The Human Semant c Web entire healt
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The Human Semant c Web If your answ
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The Human Semant c Web In the Knowl
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The Human Semant c Web ing Environm
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The Human Semant c Web Takeuchi, H.
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26 Between February 2001 and Februa
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Chapter.III General Adaptat on Fram
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General Adaptat on Framework • Da
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General Adaptat on Framework Proact
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General Adaptat on Framework semant
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General Adaptat on Framework Figure
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Figure 4. General Adaptation Framew
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General Adaptation Framework 75 thr
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Figure 11. Use case scenario of the
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General Adaptat on Framework and co
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Figure 15. Template-based transform
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Conclusion General Adaptat on Frame
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General Adaptat on Framework Statis
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General Adaptat on Framework As men
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General Adaptat on Framework METEOR
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General Adaptat on Framework 8 Java
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Ontology Creat on Methodolog es obt
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Ontology Creat on Methodolog es 0 I
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Ontology Creat on Methodolog es 0 s
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Table 1. Ontology development 101 m
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Table 2. OTK methodology Ontology C
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Ontology Creat on Methodolog es A.C
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Table 5. TOVE methodology Ontology
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Table 6. DILIGENT methodology Ontol
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Ontology Creat on Methodolog es Nor
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A Tool for Work ng w th Web Ontolog
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Figure 7. A snapshot of the tier hi
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Bu ld ng Ontology Translat on Syste
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Table 1. Examples of transformation
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6 http://km.aifb.uni-karlsruhe.de/w
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Query ng the Web Recons dered allow
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Query ng the Web Recons dered rathe
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Query ng the Web Recons dered Three
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Query ng the Web Recons dered retri
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Answers:.Arbitrary.XML,.Ranked Answ
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ib {{ book {{ title { “Data on th
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Specific Reasoning as Theories Quer
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Versatility Query ng the Web Recons
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Query ng the Web Recons dered 0 (Se
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Query ng the Web Recons dered Furch
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Section IV Applications Query ng th
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Semant c E-Bus ness to be beneficia
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Semant c E-Bus ness The following s
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Semant c E-Bus ness standardized re
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Intelligent Agents Semant c E-Bus n
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Semant c E-Bus ness other form of e
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Semant c E-Bus ness Figure 3. Seman
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Semant c E-Bus ness upon the transp
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Semant c E-Bus ness use of agent-ba
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Semant c E-Bus ness similation of e
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Semant c E-Bus ness Papazoglou, M.
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Distributed Patient Identification
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Page 314 and 315: About.the.Authors About the Authors
Page 316 and 317: About the Authors 0 Veli. Bicer. is
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Page 320 and 321: About the Authors 0 partment (1992)
Page 322 and 323: About the Authors Anton.Naumenko.is
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Page 326 and 327: consumer-centric business model 30
Page 328 and 329: semantic e-business 214 semantic in
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