en
0.25
0.5
0.75
1.25
1.5
1.75
2
Detecting Incorrect Numerical Data in DBpedia
Published on Jul 30, 20143474 Views
DBpedia is a central hub of Linked Open Data (LOD). Being based on crowd-sourced contents and heuristic extraction methods, it is not free of errors. In this paper, we study the application of unsuper
Related categories
Chapter list
Detecting Incorrect Numerical Data in DBpedia00:00
Motivation - 100:00
Motivation - 200:30
Motivation - 302:11
Idea03:50
Approach04:32
Median Absolute Deviation (MAD)04:58
Interquartile Range05:24
Kernel Density Estimation05:50
Approach - 106:18
Approach - 207:15
Evaluation08:03
Evaluation: Pre-study - 109:05
Evaluation: Pre-study - 209:30
Evaluation: Random sample - 110:11
Evaluation: Random sample - 211:05
Systematic Errors Found - 111:58
Systematic Errors Found - 213:08
Systematic Errors Found - 314:02
Limitations14:53
Beyond DBpedia15:43
Ongoing work16:41
Questions?18:39
Thank you!18:44