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

Recommendations

Info

Chapter.I Semant cs for the Semant c Web Semantics.for.the. Semantic.Web: The.Implicit,.the.Formal,............ and.the.Powerful Am t Sheth, Un vers ty of Georg a, USA Cart c Ramakr shnan, Un vers ty of Georg a, USA Chr stopher Thomas, Un vers ty of Georg a, USA Abstract Enabling applications that exploit heterogeneous data in the Semantic Web will require us to harness a broad variety of semantics. Considering the role of semantics in a number of research areas in computer science, we organize semantics in three forms — implicit, formal, and powerful — and explore their roles in enabling some of the key capabilities related to the Semantic Web. The central message of this article is that building the Semantic Web purely on description logics will artificially limit its potential, and that we will need to both exploit well-known techniques that support implicit semantics, and develop more powerful semantic techniques. Copyright © 2007, Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited.
Sheth, Ramakr shnan, & Thomas Introduction Semantics has been a part of several scientific disciplines, both in the realm of computer science and outside of it. Research areas such as information retrieval (IR), information extraction (IE), computational linguistics (CL), knowledge representation (KR), artificial intelligence (AI), and data(base) management (DB) have all addressed issues pertaining to semantics in their own ways. Most of these areas have very different views of what “meaning” is, and these views are all built on some meta-theoretical and epistemological assumptions. These different views imply very different views of cognition, of concepts, and of meaning (Hjorland, 1998). In this chapter, we organize these different views to three forms of semantics: implicit, formal, and powerful (a.k.a. soft). We use these forms to explore the role of semantics that go beyond the narrower interpretation of the Semantic Web (that involve adherence to contemporary Semantic Web standards) and encompass those required for a broad variety of semantic applications. We advocate that for the Semantic Web (SW) to be realized, we must harness the power of a broad variety of semantics encompassing all three forms. IR, IE, and CL techniques primarily draw upon analysis of unstructured texts in addition to document repositories that have a loosely defined and less formal structure. In these sorts of data sources, the semantics are implicit. In the fields of KR, AI, and DB, however, the data representation takes a more formal and/or rigid form. Well-defined syntactic structures are used to represent information or knowledge where these structures have definite semantic interpretations associated with them. There are also definite rules of syntax that govern the ways in which syntactic structures can be combined to represent the meaning of complex syntactic structures. In other words, techniques used in these fields rely on formal semantics. Usually, efforts related to formal semantics have involved limiting expressiveness to allow for acceptable computational characteristics. Since most KR mechanisms and the relational data model are based on set theory, the ability to represent and utilize knowledge that is imprecise, uncertain, partially true, and approximate is lacking, at least in the base/standard models. However, there have been several efforts to extend the base models (e.g., Barbara, Garcia-Molina, & Porter, 1992). Representing and utilizing these types of more powerful knowledge is, in our opinion, critical to the success of the Semantic Web. Soft computing has explored these types of powerful semantics. We deem these powerful (soft) semantics as distinguished, albeit not distinct from or orthogonal to formal and implicit semantics. More recently, semantics has been driving the next generation of the Web as the Semantic Web, where the focus is on the role of semantics for automated approaches to exploiting Web resources. This involves two well-recognized, critical enabling capabilities — ontology generation (Maedche & Staab, 2001; Omelayenko, 2001) 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 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 84 and 85:
General Adaptat on Framework Figure
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 ...

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?