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Actively Learning to Rank Semantic Associations for Personalized Contextual Exploration of Knowledge Graphs
Published on Jul 10, 2017961 Views
Knowledge Graphs (KG) represent a large amount of Semantic Associations (SAs), i.e., chains of relations that may reveal interesting and unknown connections between different types of entities. Applic
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Chapter list
Actively Learning to Rank Semantic Associations for Personalized Contextual Exploration of Knowledge Graphs00:00
Outline00:14
Untitled00:35
Knowledge Graphs (KGs)00:35
Relational Knowledge in KGs and Semantic Associations01:03
Empowering Comprehension of Web Content01:33
Empowering Comprehension of Web Content - 101:52
Contextual Exploration of KGs02:02
Contextual Exploration of KGs - 102:21
Contextual Exploration of DBpedia with DaCENA02:33
Entity Extraction 03:18
Retrieval of Semantic Associations03:32
Retrieval of Semantic Associations - 103:55
Information Overload in Contextual KG Exploration03:59
Ranking SAs by Estimated Interest: Serendipity 04:26
Ranking SAs by Estimated Interest: Serendipity - 104:35
Ranking SAs by Estimated Interest: Serendipity - 205:20
Example of SAs ranked by Serendipity05:37
Outline06:24
Personalized Exploration of KGs06:31
Active Learning to Rank for SAs07:28
Active Learning to Rank for SAs - 107:43
Active Learning to Rank for SAs - 208:03
Active Learning to Rank for SAs - 308:27
Active Learning to Rank for SAs - 408:43
Active Learning to Rank for SAs - 508:50
Clustering as Bootstrapping09:00
Clustering as Bootstrapping - 109:10
Clustering as Bootstrapping - 209:16
Clustering as Bootstrapping - 309:20
Serendipity as Bootstrapping09:28
Serendipity vs Clustering09:48
Example: Rating of Most Serendipitous SAs (#0)10:27
Example: Rating of Most Serendipitous SAs (#0) - 110:41
Example: Ranking Learned with RankSVM (#0)10:48
Example: Ranking Learned with RankSVM (#0) - 110:51
Example: Rating on Sampled SAs (#1)11:15
Example: Rating on Sampled SAs (#1) - 111:21
Untitled11:27
Example: Ranking Learned with RankSVM (#1)11:28
Example: Ranking Learned with RankSVM (#1) - 111:39
Features for RankSVM11:49
Experiments: Objectives12:30
Experiments: Settings13:01
Experiments: Data13:47
Experiments: Alternative Configurations and Baselines14:31
Results: Personalization Hypothesis15:36
Untitled16:16
Results: Performance (nDCG@10)16:17
Results: Performance (nDCG@10) - 116:40
Results: Performance (nDCG@10) - 216:48
Results: Performance (nDCG@10) - 317:00
Conclusions and Future Work17:22
Questions18:21