Tutorial: Inference in Statistical Relational AI

1. Abstract

In recent years, a need for efficient inference algorithms on compact representations of large relational databases became apparent, e.g., in natural language understanding, machine learning, or decision making. This need has lead to advances in probabilistic relational modeling for artificial intelligence (also called statistical relational AI). Probabilistic relational models combine the fields of reasoning under uncertainty and modeling
incorporating relations and objects in the vain of first-order logic. After briefly introducing basics about probability theory and Bayesian or Markovian networks, we present specific probabilistic relational modelling approaches and focus on exact and approximative inference algorithms.

2. Names and Affiliations

• Tanya Braun, Institute of Information Systems, University of Lübeck
  Email: braun@ifis.uni-luebeck.de
  Website: https://www.ifis.uni-luebeck.de/index.php?id=braun
• Marcel Gehrke, Institute of Information Systems, University of Lübeck
  Email: gehrke@ifis.uni-luebeck.de
  Website: https://www.ifis.uni-luebeck.de/index.php?id=gehrke

3. Outline (tentative)

1. Introduction
2. Probabilistic (relational) modeling
   • Semantics
   • Inference problems and applications
   • Algorithms and systems
   • Scalability
3. Scalability by lifting
   • Exact lifted inference
   • Approximate lifted inference
4. Summary