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Linear temporal logic (LTL) is a modal logic where formulas are built over temporal operators relating events happening in different time instants. According to the standard semantics, LTL formulas are interpreted on traces spanning over an infinite timeline. However, applications related to the specification and verification of business processes have recently pointed out the need for defining and reasoning about a variant of LTL, which we name LTLp, whose semantics is defined over process traces, that is, over finite traces such that, at each time instant, precisely one propositional variable (standing for the execution of some given activity) evaluates true. The paper investigates the theoretical underpinnings of LTLp and of a related logic formalism, named LTLf, which had already attracted attention in the literature and where formulas have the same syntax as in LTLp and are evaluated over finite traces, but without any constraint on the number of variables simultaneously evaluating true. The two formalisms are comparatively analyzed, by pointing out similarities and differences. In addition, a thorough complexity analysis has been conducted for reasoning problems about LTLp and LTLf, by considering arbitrary formulas as well as classes of formulas defined in terms of restrictions on the temporal operators that are allowed. Finally, based on the theoretical findings of the paper, a practical reasoner specifically tailored for LTLp and LTLf has been developed by leveraging state-of-the-art SAT solvers. The behavior of the reasoner has been experimentally compared with other systems available in the literature.

We tackle fact checking using Knowledge Graphs (KGs) as a source of background knowledge. Our approach leverages the KG schema to generate candidate evidence patterns, that is, schema-level paths that capture the semantics of a target fact in alternative ways. Patterns verified in the data are used to both assemble semantic evidence for a fact and provide a numerical assessment of its truthfulness. We present efficient algorithms to generate and verify evidence patterns, and assemble evidence. We also provide a translation of the core of our algorithms into the SPARQL query language. Not only our approach is faster than the state of the art and offers comparable accuracy, but it can also use any SPARQL-enabled KG.

Research in systems biology has made available large amounts of data about interactions among cell building blocks (e.g., proteins, genes). To properly look up these data and mine useful information, the design and development of automatic tools has become crucial. These tools leverage Biological Networks as a formal model to encode molecular interactions. Biological networks can be fed as input to graph-based techniques useful to infer new information about cellular activity and evolutive processes of the species. In this context, a rather interesting family of techniques is that of network querying. Network querying tools search a whole biological network to identify conserved occurrences of a given query module for transferring biological knowledge. Indeed, inasmuch as the query network generally encodes a well-characterized functional module, its occurrences in the queried network suggest that the latter (and, as such, the corresponding organism) features the function encoded by the former. The aim of this paper is that of analyzing and comparing tools devised to query biological networks. This analysis is intended to help in understanding problems and research issues, state of the art and opportunities for researchers working in this area.