Seminar on Internet Technologies (Winter 2017/2018): Difference between revisions

Seminar on Internet Technologies (Winter 2017/2018) (view source)

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+| '''A Review of Relational Machine Learning for Knowledge Graphs'''
+Traditional machine learning algorithms take as input a feature vector, which represents an object in terms of numeric or categorical attributes. The main learning task is to learn a mapping from this feature vector to an output prediction of some form. In Statistical Relational Learning (SRL), the representation of an object can contain its relationships to other objects. Thus the data is in the form of a graph, consisting of nodes (entities) and labelled edges (relationships between entities). The main goals of SRL include prediction of missing edges, prediction of properties of nodes, and clustering nodes based on their connectivity patterns. The task is to review a variety of techniques from the SRL community and explain how they can be applied to large-scale knowledge graphs (KGs), i.e., graph structured knowledge bases (KBs) that store factual information in form of relationships between entities.
+|[Bo Zhao (bo.zhao@gwdg.de)]
+|[http://ieeexplore.ieee.org/document/7358050/]
+|-
 |}