Automating RDF Dataset Transformation and Enrichment thumbnail
slide-image
Pause
Mute
Subtitles not available
Playback speed
0.25
0.5
0.75
1
1.25
1.5
1.75
2
Full screen

Automating RDF Dataset Transformation and Enrichment

Published on Jul 15, 20151748 Views

With the adoption of RDF across several domains, come growing requirements pertaining to the completeness and quality of RDF datasets. Currently, this problem is most commonly addressed by manually

Related categories

Chapter list

DEER Automating RDF Dataset Transformation and Enrichment00:00
Outline - 100:25
Outline - 200:36
Why RDF Transformation & Enrichment? - 100:40
Why RDF Transformation & Enrichment? - 201:13
RDF Transformation & Enrichment?01:29
Manual Knowledge base Enrichment - 102:15
Manual Knowledge base Enrichment - 202:33
Automatic Knowledge base Enrichment - 102:52
Automatic Knowledge base Enrichment - 203:37
Outline - 304:00
Atomic Enrichment Functions - 104:01
Atomic Enrichment Functions - 204:31
Atomic Enrichment Functions - 304:40
Atomic Enrichment Functions - 405:04
Self Configuration - 105:09
Self Configuration - 205:11
Self Configuration - 305:48
Self Configuration - 406:02
Self Configuration - 506:06
Self Configuration - 606:12
Self Configuration - 706:24
Self Configuration - 806:37
Atomic Enrichment Functions - 106:49
Atomic Enrichment Functions - 206:53
Atomic Enrichment Functions - 306:54
Atomic Enrichment Functions - 406:57
Self Configuration - 907:00
Self Configuration - 1007:05
Self Configuration - 1107:09
Atomic Enrichment Functions - 507:12
Atomic Enrichment Functions - 607:16
Atomic Enrichment Functions - 707:18
Self Configuration - 1207:42
Self Configuration - 1307:49
Self Configuration - 1408:00
KB Enrichment Refinement Operator - 108:52
KB Enrichment Refinement Operator - 209:20
KB Enrichment Refinement Operator - 309:33
Positive Example09:40
Learning Algorithm - 110:09
Learning Algorithm - 210:18
Learning Algorithm - 310:27
Learning Algorithm - 410:31
Learning Algorithm - 510:43
Learning Algorithm - 610:45
Learning Algorithm - 710:47
Learning Algorithm - 810:48
Most Promising Node Selection - 111:00
Most Promising Node Selection - 211:19
Most Promising Node Selection - 311:44
Outline - 411:55
Experimental Setup - 111:59
Experimental Setup - 212:47
Configuration of the Search Strategy13:04
Effect of Positive Examples13:44
Outline - 515:00
Conclusion and Future Work - 115:04
Conclusion and Future Work - 215:22
Thank You!16:12