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Exception-enriched Rule Learning from Knowledge Graphs
Published on Nov 10, 20161240 Views
Advances in information extraction have enabled the automatic construction of large knowledge graphs (KGs) like DBpedia, Freebase, YAGO and Wikidata. These KGs are inevitably bound to be incomplete. T
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Chapter list
Exception-enriched Rule Learning from Knowledge Graphs00:00
Knowledge Graphs 00:19
Mining Rules from KGs - 100:49
Mining Rules from KGs - 201:41
Mining Rules from KGs - 302:25
Our Goal02:45
Problem Statement - 103:08
Problem Statement - 204:07
Problem Statement: Conflicting Predictions - 104:21
Problem Statement: Conflicting Predictions - 205:07
Problem Statement: Conflicting Predictions - 305:51
Approach Overview06:31
Step 1: Mining Horn Rules06:46
Step 2: Extracting Exception Witness Set06:46
Step 3: Constructing Candidate Revisions08:17
Step 4: Selecting the Best Revision08:32
Ranking Rules Revisions - 109:53
Ranking Rules Revisions - 210:06
Ranking Rules Revisions - 310:22
Experiments - 110:54
Experiments - 212:29
Experiments - 313:04
Summary13:52