A Tutorial on Query Answering and Reasoning over Probabilistic Knowledge Bases

摘要

Large-scale probabilistic knowledge bases are becoming increasingly important in academia and industry alike. They are constantly extended with new data, powered by modern information extraction tools that associate probabilities with knowledge base facts. This tutorial is dedicated to give an understanding of various query answering and reasoning tasks that can be used to exploit the full potential of probabilistic knowledge bases. In the first part of the tutorial, we focus on (tuple-independent) probabilistic databases as the simplest probabilistic data model. In the second part of the tutorial, we move on to richer representations where the probabilistic database is extended with onto-logical knowledge. For each part, we review some known data complexity results as well as discuss some recent results.