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Embedding Mapping Approaches for Tensor Factorization and Knowledge Graph Modelling
Published on Jul 28, 20161193 Views
Latent embedding models are the basis of state-of-the art statistical solutions for modelling Knowledge Graphs and Recommender Systems. However, to be able to perform predictions for new entities and
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Chapter list
Embedding Mapping Approaches for Tensor Factorization and Knowledge Graph Modelling00:00
Agenda00:09
Introduction to Latent Embedding Models - 100:43
Introduction to Latent Embedding Models - 203:38
Motivation to Embedding Mapping04:56
Approach 0: Factorization Models with Closed-Form Mappings05:49
Approach 1: Post Mapping - 107:36
Untitled07:56
Approach 1: Post Mapping - 308:18
Approach 2: Hatting Algorithm09:07
Approach 3: Back-Propagation - 109:58
Approach 3: Back-Propagation - 210:37
Experiment 1: MovieLens Data11:53
Experiment 2: FreeBase Data13:31
Experiment 3: Amino Acid Data14:57
Conclusions and Outlooks18:50
(Selected) References21:15
Thank you for your attention21:19