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A Tutorial on Query Answering and Reasoning over Probabilistic Knowledge Bases

İsmail İlkan Ceylan and Thomas Lukasiewicz

Abstract

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 rst 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 ontological knowledge. For each part, we review some known data complexity results as well as discuss some recent results.

Book Title
Reasoning Web. Semantic Interoperability on the Web − 14th International Summer School 2018‚ Luxembourg‚ September 22−26‚ 2018‚ Tutorial Lectures
Month
September
Publisher
Springer
Series
Lecture Notes in Computer Science
Year
2018