Relational Machine Learning for Knowledge Graphs
In this colloquium Patrick Westphal will present the paper “A Review of Relational Machine Learning for Knowledge Graphs” by Nickel, Murphy, Tresp and Gabrilovich.
Abstract: Relational machine learning studies methods for the statistical analysis of relational, or graph-structured, data. In this paper, we provide a review of how such statistical models can be “trained” on large knowledge graphs, and then used to predict new facts about the world (which is equivalent to predicting new edges in the graph). In particular, we discuss two different kinds of statistical relational models, both of which can scale to massive datasets. The first is based on tensor factorization methods and related latent variable models. The second is based on mining observable patterns in the graph. We also show how to combine these latent and observable models to get improved modeling power at decreased computational cost. Finally, we discuss how such statistical models of graphs can be combined with text-based information extraction methods for automatically constructing knowledge graphs from the Web. In particular, we discuss Google’s Knowledge Vault project.
Zoo Keeper, Apache Mesos
A short introduction to the coordination system ‘ZooKeeper’ and cluster manager ‘Apache Mesos’ will be presented by Hajira.
Mesos provides efficient resource isolation and sharing across distributed applications and frameworks. It can run a multitude of applications on a dynamically shared pool of resources being coordinated by zookeeper.