|14:00 - 14:15||Opening and Welcome|
|14:15 - 15:15||Invited Talk by Volker Tresp|
|"Learning with Knowledge Graphs" presentation slides|
|15:15 - 15:30||Kemo Adrian|
|"From Learning Through Signal Processing to Argumentation on Ontological Representations"|
|15:30 - 16:00||Coffee break|
|16:00 - 16:30||Tsuyoshi Murata, Yohei Onuki, Shun Nukui, Seiya Inagi, Xule Qiu, Masao Watanabe and Hiroshi Okamoto|
|"Predicting relations between RDF entities by Deep Neural Network"|
|16:30 - 17:00||Luis Espinosa, Sergio Oramas, Horacio Saggion and Xavier Serra|
|"ELMDist: A vector space model with words and MusicBrainz entities"|
|17:00 - 17:30||Gregoire Burel, Hassan Saif, Miriam Fernandez and Harith Alani|
|"On Semantics and Deep Learning for Event Detection in Crisis Situations"|
Invited Talk "Learning with Knowledge Graphs"
Abstract. In recent years a number of large-scale triple-oriented knowledge graphs have been generated. They are being used in research and in applications to support search, text understanding and question answering. Knowledge graphs pose new challenges for machine learning, and research groups have developed novel statistical models that can be used to compress knowledge graphs, to derive implicit facts, to detect errors, and to support the above mentioned applications. Some of the most successful statistical models are based on tensor decompositions that use latent representations of the involved generalized entities. In my talk I will introduce knowledge graphs and approaches to learning with knowledge graphs. I will discuss how knowledge graphs can be related to cognitive semantic memory, episodic memory and perception. Finally, I will address the question if knowledge graphs and their statistical models might also provide insight into the brain's memory system.