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Linear Datalog programs are programs whose clauses have at most one intensional atom in their bodies. We explore syntactic classes of Datalog programs (syntactically non-linear) which turn out to express no more than the queries expressed by linear Datalog programs. In particular, we investigate linearisability of (database queries corresponding to) piecewise linear Datalog programs and chain queries:(a) We prove that piecewise linear Datalog programs can always be transformed into linear Datalog programs, by virtue of a procedure which performs the transformation automatically. The procedure relies upon conventional logic program transformation techniques.(b) We identify a new class of linearisable chain queries, referred to as pseudo-regular, and prove their linearisability constructively, by generating, for any given pseudo-regular chain query, the Datalog program corresponding to it. [Copyright &y& Elsevier]

In this paper we show how re-interpreting PageRank as an argumentation semantics for a bipolar argumentation framework empowers its explainability. After showing that PageRank, naively re-interpreted as an argumentation semantics for support frameworks, fails to satisfy some generally desirable properties, we propose a novel approach able to reconstruct PageRank as a gradual semantics of a suitably defined bipolar argumentation framework, while satisfying these properties. We then show how the theoretical advantages afforded by this approach also enjoy an enhanced explanatory power: we propose several types of argument-based explanations for PageRank, each of which focuses on different aspects of the algorithm and uncovers information useful for the comprehension of its results. [ABSTRACT FROM AUTHOR]