Subject-centric computing, Fourth International Conference on Topic Maps Research and Applications, TMRA 2008, Leipzig, Germany, October 16 - 17, 2008, revised selected papers (Leipziger Beiträge zur Informatik)

Subject-centric Computing......Page 3
Preface......Page 5
Program Committee......Page 7
Sponsoring Organizations......Page 8
Program of tutorials@TMRA 2008 Wednesday, October 15, 2008......Page 9
Topincs Hands on Topic Maps (morning session)......Page 10
TMAPI 2.0 (afternoon session)......Page 11
Fast portal programming with ActiveTM (afternoon session)......Page 12
Program of TMRA 2008 Thursday, October 16, 2008......Page 13
Program of TMRA 2008 Friday, October 17, 2008......Page 15
Standards related research......Page 19
Living Topic Maps......Page 20
Poster Session......Page 21
1. Introduction......Page 25
2. Using Topic Maps as Application Data Model......Page 26
3.1 Background......Page 27
3.4 Topic Maps Benefits for Interface Structure and Functionality......Page 28
4. Discussion......Page 31
References......Page 32
1 Introduction......Page 35
2 Sensemaking......Page 37
2.2 Semantic Linking......Page 38
2.3 Dialogue Mapping......Page 39
2.3.1 Related work......Page 40
2.4 Subject-centric Federation......Page 41
2.5 Sensemaking Processes......Page 42
2.5.1 Related work......Page 44
3 A platform for sensemaking......Page 46
3.2 REST Web Services API......Page 47
4 Discussion......Page 48
References......Page 51
1 Introduction......Page 55
2 UNIX File System......Page 57
3.2 Maps......Page 58
3.3 Topics......Page 59
3.4 Associations, Names and Occurrences......Page 60
4.1 Mounting......Page 61
4.2 Inspection......Page 62
4.3 Bulk Retrieval......Page 63
4.4 Modification......Page 64
5 Research Prototype......Page 65
6.2 Empty Files vs. No Files......Page 66
6.4 Associations......Page 67
6.7 Scalability......Page 68
7 Future Work......Page 69
References......Page 70
1 Motivation......Page 73
2 Facet Selection......Page 75
restriction values......Page 76
Frequency:......Page 77
2.3 Additional Facet Classes......Page 78
3 Architecture of the Prototype......Page 79
4 Topic Map Exploration......Page 80
4.1 Omnigator and Vizigator......Page 81
4.2 Faceted Navigation......Page 82
4.3 Discussion of Perspectives......Page 83
5 Summary and Outlook......Page 84
References......Page 85
1 Introduction......Page 87
3 Related Works......Page 88
4 Security Domain......Page 89
5.1 List Terms......Page 90
Topic Maps-based Approach......Page 91
5.3 Implementation of TMIR and CIR Systems......Page 94
6.1 Experiment Participants......Page 95
6.3 Procedure......Page 96
6.5 Search Tasks......Page 97
8 Conclusion......Page 98
References......Page 99
1 Introduction......Page 101
2.1 Quality, Relevance and Importance in the Literature......Page 102
Importance.......Page 103
4.1 Manually Assigning QRI......Page 104
Importance.......Page 105
5 Topic Map Implementation......Page 106
5.1 Listing of topics, associations and occurrence types......Page 107
5.2 Mimicking Fuzzy Neural Networks......Page 109
Importance.......Page 110
6.1 The Basic Model......Page 111
6.2 Ranking in the Context of a Specific Topic......Page 112
6.3 Ranking in Keyword Search......Page 113
6.6 Partial Implementation on fuzzzy.com......Page 114
7 Concluding Remarks......Page 115
References......Page 116
5. Improve the learning curve.......Page 121
Unknown......Page 0
2.1 introduce mixed content in topic names......Page 122
element......Page 123
2.3 introduce mixed content in variants......Page 124
2.4 introduce mixed content in occurrences......Page 125
3.1 use attributes whenever possible......Page 126
controversy......Page 127
4.2 Controversy around......Page 128
with the IRI......Page 129
4.3 Ensure completeness......Page 131
6 Acknowledgement......Page 132
References......Page 133
A A sample XTM 3.0 file......Page 134
B The RelaxNG schema......Page 135
1 Introduction......Page 137
2 Manual for GTM......Page 138
2.1 How to represent topics and types relations?......Page 140
2.2 How to represent Topic Names?......Page 141
2.3 How to represent a subject identity?......Page 143
2.4 How to represent occurrences?......Page 144
2.5 How to represent an association?......Page 146
2.6 How to represent reification?......Page 147
3 Domain and Subject Centric Views......Page 148
4 Summary and Outlook......Page 150
References......Page 151
1 Introduction......Page 153
3 Status......Page 154
4.1 Changes in core......Page 156
Role......Page 157
5.1 Filter Language......Page 158
References......Page 160
1 Introduction......Page 161
1.1 The RTM mapping......Page 162
2 Comparing TMCL with RDFS/OWL......Page 163
2.2 Consequences for the conversion......Page 164
3 RDFS to TMCL conversion......Page 165
4.1 Class and property relationships......Page 168
4.2 Cardinalities......Page 169
4.4 Input to mapping......Page 170
4.6 Various elements......Page 172
4.7 Non-convertible elements......Page 173
5.2 The conversion......Page 174
5.3 Abstract classes......Page 175
6 Related work......Page 176
7 Conclusion and further work......Page 177
References......Page 178
1 Introduction......Page 181
2 Building Distributed Applications with XTM, TMQL, TMAPI etc.......Page 183
3 P2P applications......Page 184
5 Knowledge Exchange Protocol (KEP)......Page 185
6 KEP scenarios – Internet peers......Page 186
7 KEP scenario – Peers in spontaneous networks......Page 187
8 Peer API / Knowledge Ports......Page 188
9 Shark Engine......Page 189
10 Shark peers versus software agents......Page 190
References......Page 191
1 Introduction and motivation......Page 193
2 Making each piece of information accessible through various contexts......Page 195
3 Proof of concept for EADS......Page 196
4 The uniform information layer......Page 198
5 A combination of three analysis procedures......Page 200
6 Querying associated information......Page 202
References......Page 203
1 Introduction......Page 207
1.1 Assessment......Page 208
2.1 TMDM interfaces layer (read-write access)......Page 209
Scopeable and Scope.......Page 210
2.2 TMDM interfaces layer (read-only access)......Page 211
2.4 TMDM Merged implementation layer (read-only access)......Page 213
Merging topics.......Page 214
Dependent merging.......Page 215
Merging complexity.......Page 216
Unmerging complexity.......Page 217
2.5 TM4J1 compatibility layer......Page 218
2.6 TopicMapEventListener......Page 219
2.7 Architectural overview......Page 221
3 Evaluation......Page 222
4 Future Work......Page 224
Acknowledgements......Page 225
References......Page 226
1 Introduction......Page 227
2 Previous Approaches......Page 228
2.1 A Basic Read Operation using TMAPI......Page 229
2.2 Topic Maps Objects......Page 230
2.3 Bogachev’s Subject-Centric Programming Language......Page 231
2.4 The Active Record Design Pattern......Page 232
3 Domain Modeling......Page 234
4.1 ActiveTMML......Page 235
4.2 Definition of Model Classes in ActiveTM......Page 237
4.3 Usage of ActiveTM Objects......Page 238
5 Code Generation......Page 239
6 Ontology Introspection......Page 240
References......Page 241
1 Introduction......Page 243
2 Design Overview......Page 244
3 Push vs. Pull API......Page 246
5 Conclusions and Further Work......Page 247
References......Page 248
Scope......Page 249
Server Contract......Page 250
Topic Maps Feed......Page 251
Topic Map Feed......Page 252
Snapshot Feed......Page 253
Topic Map Fragments Feed......Page 254
Topic Map Fragment Data Service......Page 255
A Clean start......Page 256
Note:......Page 257
References......Page 258
1 Introduction......Page 261
2 Original application used at the Observatory......Page 262
3 Topic Maps document creation......Page 263
4 Comparison of Topic Maps tools......Page 265
5 Web presentation creation......Page 266
6 Final state of the pilot application......Page 267
Acknowledgement......Page 268
References......Page 270
1 Introduction......Page 271
2.1 Change from fCourse-Centricfl tofiSubjec-Centricf......Page 273
2.2 Creating a Primitive Topic Maps Portal and Students’ Response......Page 275
Balance between "Push"  and "Pull".......Page 277
3 Construction of Topic Maps for Subject Centric Extensible eLearning......Page 278
3.1 Topic Maps Ontology of EPEL......Page 279
3.2 Multi-Layered Structure of Topic Types......Page 282
References......Page 283
1 Introduction......Page 285
2 Related topic maps......Page 286
Ontology making......Page 287
Application development......Page 288
3.2 Topic Maps case examples topic map......Page 289
3.3 Topic Maps case examples topic map application......Page 290
4 Issues and discussion......Page 292
4.2 Classification scheme......Page 293
5 Conclusion and Future Work......Page 294
References......Page 295
1 Introduction......Page 299
3 Permanent Transclusion......Page 302
4 Item Copying......Page 303
5.1 Temporary transclusion and item copying......Page 304
5.2 Permanent transclusion and agile extension......Page 305
6 Problems......Page 306
7 Conclusion and Future Work......Page 307
References......Page 308
SocioTM – Relevancies, Collaboration, and Socio-knowledge in Topic Maps......Page 309
SocioTM......Page 310
1.1 Current state......Page 311
KnAlledge......Page 312
2.1. Implementation......Page 313
3 Relevancies evolution......Page 314
TM......Page 315
4.2 Wide normalization......Page 316
4.3 Explicit normalizing with user explicit-socio-knowledge......Page 317
5.2 View-clipping......Page 318
6 SocioTM Navigation (socio-potential-law)......Page 319
7 Collaboration within Topic Maps......Page 320
8 SocioTM implementation......Page 321
References......Page 322
1 Introduction......Page 325
2.2 The Usefulness of Serendipity in IR......Page 326
3.2. Findings......Page 327
4.1. Use of the Topic Map Components......Page 328
4.2. Topic Map Components Inducive to Serendipitous Discoveries......Page 329
5 Suggestions from Participants......Page 330
5.2 Occurrence Type as Component: Strengths and Desired Improvements A. Presentation of Details......Page 331
6.2. Semantic Labeling......Page 332
7. Conclusion......Page 333
References......Page 334
Making Metadadate Alive: Migrating Metadata into Richer Semantic Relationships Using Topic Maps-based Ontology......Page 337
RDF to Topic Map, Compatibilities and Differences in Design Process for Bam 3DCG Ontology......Page 338
Use of Topic Maps to support Learning Organizational Memory......Page 339