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Quality Assessment of Linked Datasets using Probabilistic Approximations
Published on Feb 4, 20251732 Views
With the increasing application of Linked Open Data, assessing the quality of datasets by computing quality metrics becomes an issue of crucial importance. For large and evolving datasets, an exact,
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Presentation
Quality Assessment of Linked Datasets using Probabilistic Approximation00:00
The Problem - 100:18
The Problem - 200:26
The Problem - 300:29
Hypothesis01:04
Overview - 101:24
Overview - 202:29
Overview - 302:59
Reservoir Sampling03:11
Dereferenceability - 103:27
Dereferenceability - 203:47
Dereferenceability - 303:54
Dereferenceability - 404:00
Dereferenceability - 504:03
Dereferenceability - 604:09
Dereferenceability - 704:13
Dereferenceability - 804:17
Dereferenceability - 904:37
Dereferenceability - 1004:48
Dereferenceability - 1104:54
Dereferenceability - 1204:59
Dereferenceability - 1305:00
Dereferenceability - 1405:04
Dereferenceability - 1505:04
Dereferenceability - 1605:11
Dereferenceability - 1705:19
Dereferenceability - 1805:26
Experiment Results - 105:37
Experiment Result - Time - 106:20
Experiment Result - Time - 206:35
Links to External Data Providers06:56
Links to External Data - 107:04
Links to External Data - 207:08
Links to External Data - 307:08
Links to External Data - 407:34
Links to External Data - 507:54
Links to External Data - 607:58
Links to External Data - 708:04
Links to External Data - 808:09
Experiment Results - 208:27
Experiment Result - Time - 309:02
Experiment Result - Time - 409:19
Sum Up (I)09:32
Overview - 410:07
Bloom Filters - 110:15
Bloom Filters - 210:24
Bloom Filters - 310:53
Bloom Filters - 410:56
Bloom Filters - 511:02
Bloom Filters - 611:09
Bloom Filters - 711:31
Bloom Filters - 811:36
Extensional Conciseness - 111:37
Extensional Conciseness - 211:49
Extensional Conciseness - 312:00
Extensional Conciseness - 412:14
Extensional Conciseness - 512:22
Extensional Conciseness - 612:39
Extensional Conciseness - 713:13
Extensional Conciseness - 813:21
Extensional Conciseness - 913:26
Experiment Results - 313:44
Sum Up (II)14:36
Overview - 515:00
Clustering Coefficient Estimation15:13
Clustering Coefficient of a Network - 115:21
Clustering Coefficient of a Network - 215:42
Clustering Coefficient of a Network - 315:50
Clustering Coefficient of a Network - 415:52
Clustering Coefficient of a Network - 516:00
Clustering Coefficient of a Network - 616:05
Clustering Coefficient of a Network - 716:26
Clustering Coefficient of a Network - 816:27
Clustering Coefficient of a Network - 916:36
Clustering Coefficient of a Network - 1016:40
Clustering Coefficient of a Network - 1116:41
Clustering Coefficient of a Network - 1216:48
Experiment Results - 417:18
Experiment Result - Time - 518:16
Sum Up (III)18:48
Conclusion: Lessons Learned - 119:02
Conclusion: Lessons Learned - 219:11
Conclusion: Lessons Learned - 319:21
Conclusion: Lessons Learned - 419:43
Conclusion: Lessons Learned - 520:10