JELIA 2019 features two main keynotes. Short info about the two speakers are reported below.
Title: Vadalog: Recent Advances and Applications
Abstract: Vadalog is a logic-based reasoning language for modern AI applications, in particular for knowledge graph systems. In this paper, we present recent advances and applications, with a focus on the language Vadalog itself. We first give an easy-to-access self-contained introduction to warded Datalog+/-, the logical core of Vadalog. We then discuss some recent advances: Datalog rewritability of warded Datalog+/-, and the piece-wise linear fragment of warded Datalog+/- that achieves space efficiency. We then proceed with some recent practical applications of the Vadalog language: detection of close links in financial knowledge graphs, as well as the detection of family-owned businesses.
Georg Gottlob is a Professor of Informatics at Oxford University and at TU Wien. His interests include KR, theory of data and knowledge bases, logic and complexity, problem decompositions, and, on the more applied side, web data extraction, and database query processing. Gottlob has received the Lovelace Medal (UK), the Wittgenstein Award (Austria), is an ACM Fellow, an ECCAI Fellow, a Fellow of the Royal Society, and a member of the Austrian Academy of Sciences, the German National Academy of Sciences, and the Academia Europaea. He chaired the Program Committees of IJCAI 2003 and ACM PODS 2000. He was the main founder of Lixto, a company that provides tools and services for semi-automatic web data extraction which was acquired by McKinsey & Company in 2013. Gottlob was awarded an ERC Advanced Investigator's Grant for the project "DIADEM: Domain-centric Intelligent Automated Data Extraction Methodology". Based on results of this project, he co-founded Wrapidity Ltd, a company that specializes in fully automated web data extraction that was recently acquired by an international media intelligence firm. With his collaborators he recently founded the knowledge graph start-up DeepReason.ai.
Title: Possibilistic logic: From certainty-qualified statements to two-tiered logics – A prospective survey
Abstract: Possibilistic logic (PL) is more than thirty years old. The paper proposes a survey of its main developments and applications in artificial intelligence, together with a short presentation of works in progress. PL amounts to a classical logic handling of certainty-qualified statements. Certainty is estimated in the setting of possibility theory as a lower bound of a necessity set-function. An elementary possibilistic formula is a pair made of a classical logic formula, and a certainty level belonging to a bounded scale. Basic PL handles only conjunctions of such formulas, and PL bases can be viewed as classical logic bases layered in terms of certainty. Semantics is in terms of epistemic states represented by fuzzy sets of interpretations. A PL base is associated with an inconsistency level above which formulas are safe from inconsistency. Applications include reasoning with default rules, belief revision, Bayesian possibilistic networks, information fusion, and preference modeling (in this latter case, certainty is turned into priority). Different extensions of basic PL are briefly reviewed, where levels take values in lattices, are replaced by vectors of levels, or are handled in a purely symbolic manner (without being instantiated). This latter extension may be of interest for explanation purposes. A paraconsistent treatment of inconsistency is also discussed. Still another extension allows for associating possibilistic formulas with sets of agents or sources that support them. In generalized possibilistic logic (GPL), negation and disjunction can be applied as well as conjunction, to possibilistic formulas. It may be viewed as a fragment of modal logic (such as KD45) where modalities cannot be nested. GPL can be still extended to a logic involving both objective and non-nested multimodal formulas. Applications of GPL to the modeling of ignorance, to the representation of answer set programs, to reasoning about other agents’ beliefs, and to a logic of argumentation are outlined. Generally speaking, the interest and the strength of PL relies on a sound alliance between classical logic and possibility theory which offers a rich representation setting allowing an accurate modeling of partial ignorance. The paper focuses more on ideas than on technicalities and provides references for details.
Henri Prade is a CNRS researcher at IRIT, Toulouse, France. He co-authored (with Didier Dubois) two monographs on fuzzy sets and possibility theory, and contributed about 250 journal papers and 350 international conference papers. He co-edited several books including the "Handbooks of Fuzzy Sets Series" (Kluwer, 7 vol, 1998-2000) with Didier Dubois, "Computational Approaches to Analogical Reasoning" (Springer, 2014) with G. Richard, as well as a "A Guided Tour of Artificial Intelligence Research" (with Pierre Marquis and Odile Papini, 3 vol., Springer, 2019). He is a member of the editorial board of several international journals; he received a Pioneer Award of the IEEE Neural Networks Society in 2002. His work dealt with uncertainty and preference modeling, reasoning under uncertainty, inconsistency, or incomplete information, analogical reasoning, or the handling of uncertainty in information systems. His present interests include possibilistic logic, analogical proportions, the structures of opposition, the synergy between learning and reasoning.