Recent Advances in Querying Probabilistic Knowledge Bases

Recent Advances in Querying Probabilistic Knowledge Bases

Stefan Borgwardt, İsmail İlkan Ceylan, Thomas Lukasiewicz

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence
Survey track. Pages 5420-5426. https://doi.org/10.24963/ijcai.2018/765

We give a survey on recent advances at the forefront of research on probabilistic knowledge bases for representing and querying large-scale automatically extracted data. We concentrate especially on increasing the semantic expressivity of formalisms for representing and querying probabilistic knowledge (i) by giving up the closed-world assumption, (ii) by allowing for commonsense knowledge (and in parallel giving up the tuple-independence assumption), and (iii) by giving up the closed-domain assumption, while preserving some computational properties of query answering in such formalisms.
Keywords:
Uncertainty in AI: Exact Probabilistic Inference
Knowledge Representation and Reasoning: Logics for Knowledge Representation
Uncertainty in AI: Uncertainty in AI