StaRAI or StaRDB?
A tutorial on Statistical Relational AI (StaRAI) at BTW 2019
Abstract
In recent years, a need for compact representations of large relational databases became apparent, e.g., in natural language understanding or decision making. Using inductive logic programming (ILP), one can build a model of a database, allowing for a crisp reproduction of data. Another idea is to build a so called factor graph model of data and introduce a probability distribution to reproduce data approximately. Such a model defines an intensional representation of a probabilistic database. A factor graph model uses parameterised variables similarly to the variables in ILP to compactly represent relations and objects. Grounding such a model incurs an exponential blowup and makes inference infeasible. Instead of grounding out a model, one can answer queries on the model directly and in a scalable way.
This tutorial aims at connecting databases and StarAI, demonstrating how database systems can benefit from methods developed within StarAI, e.g., for implementing effi- cient systems combining databases and StarAI. Thus, the goal of this tutorial is two-fold:
- Present an overview of methods within StarAI.
- Provide a forum to members of both communities for exchanging ideas.
An accompanying proceedings article can be found here.
Presenter
- Tanya Braun (University of Lübeck at the time)