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Exception-enriched Rule Learning from Knowledge Graphs
Published on 2016-11-101243 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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Presentation
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