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

Recommendations

Info

General Adaptat on Framework Figure 5. Architecture of the SmartResource prototype environment, v. 1.0 RSCDF format, three sample adapters were implemented (for a device, an expert, and a Web service). Their logic was encapsulated in three Enterprise Java Beans (EJB) and executed on the JBoss application server. Specification of the KF-330 Blow Molding Machine was used for simulation of the device data (seven device parameters). Device states were generated in a form of XML entities according to the corresponding XML schemata (three different schema variations plus a canonical one). State and condition resource data have been encoded in RSCDF after the transformation and stored in a remote Joseki 12 RDF server. For creation of a local history cache, Jena 13 classes were used. Code that coordinated coherent work of the adapters and provided a control/monitoring over them was executed in the control Java Servlet. 14 Visualization of the internal processes of the prototype environment was organized using a set of Java Server Pages 15 (JSP). Demonstrations were carried out using Internet Explorer Web browser. Web service adapter incorporated a simple sample of learning algorithm (KNN method) wrapped by a Web service container using Axis 16 and Lomboz 17 (see Figure 6). The adapter using generated SOAP-client simulated software agents’ requests for learning and diagnostics. For RSCDF-XML transformations, the adapter used the approach of two-stage transformation with RDQL-templates. In the implementation of the human expert adapter, a two-stage transformation and User Interface Templates were used for flexible building of a specific human interface (Figure 7). Involvement of the JFreeChart 18 open Java library allowed generating images for representation of the device states. Human Expert is requested for diagnostics via e-mail. Copyright © 2007, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited.
Kaykova, Khr yenko, Kovtun, Naumenko, Terz yan, & Zharko Figure 6. Implementation architecture of the Web service adapter Figure 7. Implementation architecture of the human expert adapter Challenges Implementing.General....................... Adaptation. Framework There is a variety of resources intended for integration into a common SmartResource environment. For more efficient analysis, all resources were divided into Copyright © 2007, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited.
Page 2 and 3:
Semantic Web-Based Information Syst
Page 4 and 5:
Semantic Web-Based Information Syst
Page 6 and 7:
Chapter.VIII Querying.the.Web.Recon
Page 8 and 9:
the semantics in the Semantic Web.
Page 10 and 11:
the conventional Web, and the emerg
Page 12 and 13:
Chapter.I Semant cs for the Semant
Page 14 and 15:
Semant cs for the Semant c Web and
Page 16 and 17:
Semant cs for the Semant c Web •
Page 18 and 19:
Semant cs for the Semant c Web reas
Page 20 and 21:
Semant cs for the Semant c Web simi
Page 22 and 23:
Semant cs for the Semant c Web in t
Page 24 and 25:
Entity Identification/Disambiguatio
Page 26 and 27:
Question Answering Systems Semant c
Page 28 and 29:
Semant cs for the Semant c Web The
Page 30 and 31:
Semant cs for the Semant c Web Dubo
Page 32 and 33:
Semant cs for the Semant c Web Shet
Page 34 and 35: The Human Semant c Web tion between
Page 36 and 37: The Human Semant c Web Figure 2. Th
Page 38 and 39: • Databases with entry-portals
Page 40 and 41: The Human Semant c Web • Knowledg
Page 42 and 43: Conceptual.Modeling The Human Seman
Page 44 and 45: The Human Semant c Web in relations
Page 46 and 47: The Human Semant c Web along the di
Page 48 and 49: The Human Semant c Web Figure 7. Th
Page 50 and 51: The Human Semant c Web Figure 10. T
Page 52 and 53: Conceptual.Calibration The Human Se
Page 54 and 55: The Human Semant c Web Figure 12. T
Page 56 and 57: The Human Semant c Web Figure 14. S
Page 58 and 59: Figure 16. The present structure of
Page 60 and 61: The Human Semant c Web entire healt
Page 62 and 63: The Human Semant c Web If your answ
Page 64 and 65: The Human Semant c Web In the Knowl
Page 66 and 67: The Human Semant c Web ing Environm
Page 68 and 69: The Human Semant c Web Takeuchi, H.
Page 70 and 71: 26 Between February 2001 and Februa
Page 72 and 73: Chapter.III General Adaptat on Fram
Page 74 and 75: General Adaptat on Framework • Da
Page 76 and 77: General Adaptat on Framework Proact
Page 78 and 79: General Adaptat on Framework semant
Page 80 and 81: General Adaptat on Framework Figure
Page 82 and 83: Figure 4. General Adaptation Framew
Page 86 and 87: General Adaptation Framework 75 thr
Page 88 and 89: Pilot.Implementation.of.Semantic.Ad
Page 90 and 91: General Adaptat on Framework The do
Page 92 and 93: Figure 11. Use case scenario of the
Page 94 and 95: General Adaptat on Framework and co
Page 96 and 97: Figure 15. Template-based transform
Page 98 and 99: Conclusion General Adaptat on Frame
Page 100 and 101: General Adaptat on Framework • De
Page 102 and 103: General Adaptat on Framework Statis
Page 104 and 105: General Adaptat on Framework As men
Page 106 and 107: General Adaptat on Framework METEOR
Page 108 and 109: General Adaptat on Framework 8 Java
Page 110 and 111: Ontology Creat on Methodolog es obt
Page 112 and 113: Ontology Creat on Methodolog es 0 .
Page 114 and 115: Ontology Creat on Methodolog es 0 I
Page 116 and 117: Ontology Creat on Methodolog es 0 s
Page 118 and 119: Table 1. Ontology development 101 m
Page 120 and 121: Table 2. OTK methodology Ontology C
Page 122 and 123: Ontology Creat on Methodolog es A.C
Page 124 and 125: Table 5. TOVE methodology Ontology
Page 126 and 127: Table 6. DILIGENT methodology Ontol
Page 128 and 129: Ontology Creat on Methodolog es Nor
Page 130 and 131: Ontology Creat on Methodolog es dir
Page 132 and 133: Ontology Creat on Methodolog es Isu
Page 134 and 135:
Section III Ontology Creat on Metho
Page 136 and 137:
A Tool for Work ng w th Web Ontolog
Page 138 and 139:
Page 140 and 141:
Page 142 and 143:
Page 144 and 145:
Page 146 and 147:
Page 148 and 149:
Page 150 and 151:
Page 152 and 153:
Semant c Web of Language Eng neer n
Page 154 and 155:
Page 156 and 157:
OntoELAN implements the following a
Page 158 and 159:
Page 160 and 161:
Page 162 and 163:
Page 164 and 165:
Page 166 and 167:
Figure 5. A snapshot of creating a
Page 168 and 169:
Figure 7. A snapshot of the tier hi
Page 170 and 171:
Page 172 and 173:
Chapter.VII Bu ld ng Ontology Trans
Page 174 and 175:
Bu ld ng Ontology Translat on Syste
Page 176 and 177:
Page 178 and 179:
Table 1. Examples of transformation
Page 180 and 181:
Page 182 and 183:
Page 184 and 185:
Page 186 and 187:
Page 188 and 189:
Page 190 and 191:
Page 192 and 193:
Page 194 and 195:
Page 196 and 197:
Page 198 and 199:
Page 200 and 201:
6 http://km.aifb.uni-karlsruhe.de/w
Page 202 and 203:
Query ng the Web Recons dered allow
Page 204 and 205:
Query ng the Web Recons dered rathe
Page 206 and 207:
Query ng the Web Recons dered Three
Page 208 and 209:
Query ng the Web Recons dered retri
Page 210 and 211:
Answers:.Arbitrary.XML,.Ranked Answ
Page 212 and 213:
ib {{ book {{ title { “Data on th
Page 214 and 215:
Specific Reasoning as Theories Quer
Page 216 and 217:
Versatility Query ng the Web Recons
Page 218 and 219:
Query ng the Web Recons dered 0 (Se
Page 220 and 221:
Query ng the Web Recons dered 0 sen
Page 222 and 223:
Query ng the Web Recons dered Furch
Page 224 and 225:
Section IV Applications Query ng th
Page 226 and 227:
Semant c E-Bus ness to be beneficia
Page 228 and 229:
Semant c E-Bus ness The following s
Page 230 and 231:
Semant c E-Bus ness standardized re
Page 232 and 233:
Intelligent Agents Semant c E-Bus n
Page 234 and 235:
Semant c E-Bus ness other form of e
Page 236 and 237:
Semant c E-Bus ness Figure 3. Seman
Page 238 and 239:
Semant c E-Bus ness upon the transp
Page 240 and 241:
Semant c E-Bus ness use of agent-ba
Page 242 and 243:
Semant c E-Bus ness similation of e
Page 244 and 245:
Semant c E-Bus ness Papazoglou, M.
Page 246 and 247:
Distributed Patient Identification
Page 248 and 249:
Page 250 and 251:
Page 252 and 253:
Page 254 and 255:
Page 256 and 257:
Page 258 and 259:
Page 260 and 261:
Page 262 and 263:
Page 264 and 265:
Page 266 and 267:
HL7 Clinical Genomics Standard Spec
Page 268 and 269:
Page 270 and 271:
Table 2. continued HL7 Clinical Gen
Page 272 and 273:
Page 274 and 275:
Page 276 and 277:
Page 278 and 279:
Page 280 and 281:
Page 282 and 283:
Page 284 and 285:
Page 286 and 287:
Page 288 and 289:
Page 290 and 291:
Chapter.XII Web Serv ce Messages Ar
Page 292 and 293:
Web Serv ce Messages amination, or
Page 294 and 295:
Web Serv ce Messages It is also pos
Page 296 and 297:
Web Serv ce Messages The full shara
Page 298 and 299:
Web Serv ce Messages standards. For
Page 300 and 301:
Web Serv ce Messages Figure 3. An e
Page 302 and 303:
Web Serv ce Messages Figure 4 depic
Page 304 and 305:
Web Serv ce Messages are stated as
Page 306 and 307:
Figure 6. An example SimilarTo patt
Page 308 and 309:
Figure 9. The state_structure class
Page 310 and 311:
Figure 12. Target instance 8351-9
Page 312 and 313:
Web Serv ce Messages 0 Brujin, J.,
Page 314 and 315:
About.the.Authors About the Authors
Page 316 and 317:
About the Authors 0 Veli. Bicer. is
Page 318 and 319:
About the Authors 0 in refereed jou
Page 320 and 321:
About the Authors 0 partment (1992)
Page 322 and 323:
About the Authors Anton.Naumenko.is
Page 324 and 325:
About the Authors Centre and head o
Page 326 and 327:
consumer-centric business model 30
Page 328 and 329:
semantic e-business 214 semantic in
show all

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

Create successful ePaper yourself

Delete template?

Save as template?