Computing certain answers is the standard approach when data or knowledge is incomplete. This very natural concept rooted in logical validity suffers from a severe weakness: its generally intractable computational complexity. Consequently, significant effort has been made to find tractable cases, often at the expense of severe restrictions. Here we explore a different approach, relaxing not the classes of allowed queries but the very strict notion of certainty. Our starting point is that replacing certainty with asymptotic probability 1 overcomes intractability for large classes of queries. This theoretical observation was previously made for relational queries under a simple probabilistic model of uniform distribution and an infinite domain of equally likely values; these are hardly the realities of querying data. We therefore ask whether this phenomenon is robust enough to extend to other, realistic distributions, to be applicable to other data models, and to help with practical query answering. We answer all of these positively. After extending tractability via naïve evaluation to many distributions, we extend the approach to graph data, and then experimentally show that for relational and graph queries from standard TPC and LDBC benchmarks, convergence to high probability query answers is fast and practical.

The logic of information flows (LIF) has recently been proposed as a general framework in the field of knowledge representation. In this framework, tasks of procedural nature can still be modeled in a declarative, logic-based fashion. In this paper, we focus on the task of query processing under limited access patterns, a well-studied problem in the database literature. We show that LIF is well-suited for modeling this task. Toward this goal, we introduce a variant of LIF called "forward" LIF (FLIF), in a first-order setting. FLIF takes a novel graph-navigational approach; it is an XPath-like language that nevertheless turns out to be equivalent to the "executable" fragment of first-order logic defined by Nash and Lud\"ascher. One can also classify the variables in FLIF expressions as inputs and outputs. Expressions where inputs and outputs are disjoint, referred to as io-disjoint FLIF expressions, allow a particularly transparent translation into algebraic query plans that respect the access limitations. Finally, we show that general FLIF expressions can always be put into io-disjoint form.

The logic of information flows (LIF) is a general framework in which tasks of a procedural nature can be modeled in a declarative, logic-based fashion. The first contribution of this article is to propose semantic and syntactic definitions of inputs and outputs of LIF expressions. We study how the two relate and show that our syntactic definition is optimal in a sense that is made precise. The second contribution is a systematic study of the expressive power of sequential composition in LIF. Our results on composition tie in the results on inputs and outputs and relate LIF to first-order logic (FO) and bounded-variable LIF to bounded- variable FO. This article is the extended version of a paper presented at KR 2020 [ 2 ].

The logic of information flows (LIF) has recently been proposed as a general framework in the field of knowledge representation. In this framework, tasks of a procedural nature can still be modeled in a declarative, logic-based fashion. In this paper, we focus on the task of query processing under limited access patterns, a well-studied problem in the database literature. We show that LIF is well-suited for modeling this task. Toward this goal, we introduce a variant of LIF called "forward" LIF, in a first-order setting. We define FLIF^io, a syntactical fragment of forward LIF, and show that it corresponds exactly to the "executable" fragment of first-order logic defined by Nash and Ludäscher. The definition of FLIF^io involves a classification of the free variables of an expression into "input" and "output" variables. Our result hinges on inertia and determinacy laws for forward LIF expressions, which are interesting in their own right. These laws are formulated in terms of the input and output variables.

The logic of information flows (LIF) is a general framework in which tasks of a procedural nature can be modeled in a declarative, logic-based fashion. The first contribution of this paper is to propose semantic and syntactic definitions of inputs and outputs of LIF expressions. We study how the two relate and show that our syntactic definition is optimal in a sense that is made precise. The second contribution of this paper is a systematic study of the expressive power of sequential composition in LIF. Our results on composition tie in the results on inputs and outputs, and relate LIF to first-order logic (FO) and bounded-variable LIF to bounded-variable FO.

Solving complex problems can involve non-trivial combinations of distinct knowledge bases and problem solvers. The Algebra of Modular Systems is a knowledge representation framework that provides a method for formally specifying such systems in purely semantic terms. Many practical systems based on expressive formalisms solve the model expansion task. In this paper, we con- struct a solver for the model expansion task for a complex modular system from an expression in the algebra and black-box propagators or solvers for the primitive modules. To this end, we define a general notion of propagators equipped with an explanation mechanism, an extension of the algebra to propagators, and a lazy conflict-driven learning algorithm. The result is a framework for seamlessly combining solving technology from different domains to produce a solver for a combined system.

Special-purpose propagators speed up solving logic programs by inferring facts that are hard to deduce otherwise. However, implementing special-purpose propagators is a non-trivial task and requires expert knowledge of solvers. This paper proposes a novel approach in logic programming that allows (1) logical specification of both the problem itself and its propagators and (2) automatic incorporation of such propagators into the solving process. We call our proposed language P[R] and our solver SAT-to-SAT because it facilitates communication between several SAT solvers. Using our proposal, non-specialists can specify new reasoning methods (propagators) in a declarative fashion and obtain a solver that benefits from both state-of-the-art techniques implemented in SAT solvers as well as problem-specific reasoning methods that depend on the problem's structure. We implement our proposal and show that it outperforms the existing approach that only allows modeling a problem but does not allow modeling the reasoning methods for that problem.

Well-known principles of induction include monotone induction and different sorts of nonmonotone induction such as inflationary induction, induction over well-founded sets and iterated induction. In this work, we define a logic formalizing induction over well-founded sets and monotone and iterated induction. Just as the principle of positive induction has been formalized in FO(LFP), and the principle of inflationary induction has been formalized in FO(IFP), this article formalizes the principle of iterated induction in a new logic for Nonmonotone Inductive Definitions (ID-logic). The semantics of the logic is strongly influenced by the well-founded semantics of logic programming. This article discusses the formalisation of different forms of (non-)monotone induction by the well-founded semantics and illustrates the use of the logic for formalizing mathematical and common-sense knowledge. To model different types of induction found in mathematics, we define several subclasses of definitions, and show that they are correctly formalized by the well-founded semantics. We also present translations into classical first or second order logic. We develop modularity and totality results and demonstrate their use to analyze and simplify complex definitions. We illustrate the use of the logic for temporal reasoning. The logic formally extends Logic Programming, Abductive Logic Programming and Datalog, and thus formalizes the view on these formalisms as logics of (generalized) inductive definitions.