Machines are amazing learners. Humans are amazing learners. AI is best when powered by both. AI solutions that employ both machine learning and knowledge-engineered rules learn continuously from data whilst at the same time are informed by wisdom and commonsense expressed in rules. Forrester Research Vice President & Principal Analyst, Mike Gualtieri, will convey the key trends in enterprise AI and rules/decision management; and discuss how leading enterprises can use them in combination to build truly learning AI-infused applications at scale.
Existential rules, also known as Datalog+, are an expressive knowledge representation and reasoning language, which has been mainly investigated in the context of ontological query answering. This talk will first review the landscape of decidable classes of existential rules with respect to two fundamental problems, namely chase termination (does a given set of rules ensure that the chase terminates for any factbase?) and FO-rewritability (does a given set of rules ensure that any conjunctive query can be rewritten as a first-order query?). Regarding the chase, we will specifically focus on four well-known variants: the oblivious chase, the semi-oblivious (or skolem) chase, the restricted chase and the core chase. We will then study the relationships between chase termination and FO-rewritability, which have been little investigated so far. This study leads us to another fundamental problem, namely boundedness (does a given set of rules ensure that the chase terminates for any factbase within a predefined depth?). The boundedness problem was deeply investigated in the context of datalog. It is known that boundedness and FO-rewritability are equivalent properties for datalog rules. Such an equivalence does not hold for general existential rules. We will provide a characterization of boundedness in terms of chase termination and FO-rewritability for the oblivious and semi-oblivious chase variants. Interesting questions remain open. This talk will rely on results from the literature and joint work published at ICDT 2019 and IJCAI 2019.
Marie-Laure Mugnier is a professor at the University of Montpellier and the scientific leader of a research team in knowledge representation and reasoning. Her current research mainly focuses on rule-based formalisms to do reasoning on data, specifically within the Ontology-Based Data Access paradigm. She regularly publishes papers in the main international venues in artificial intelligence. Recently, she was program co-chair of the 8th International Conference on Web Reasoning and Rule Systems (RR 2014), general co-chair of the 30th workshop on Description Logics (DL 2017) and co-organised workshops associated with IJCAI 2015 & 2016 and The Web 2019.