A Logic-based Approach for Recognizing Textual Entailment Supported by Ontological Background Knowledge

We present the architecture and the evaluation of a new system for recognizing textual entailment (RTE). In RTE we want to identify automatically the type of a logical relation between two input texts. In particular, we are interested in proving the existence of an entailment between them. We conceive our system as a modular environment allowing for a high-coverage syntactic and semantic text analysis combined with logical inference. For the syntactic and semantic analysis we combine a deep semantic analysis with a shallow one supported by statistical models in order to increase the quality and the accuracy of results. For RTE we use logical inference of first-order employing model-theoretic techniques and automated reasoning tools. The inference is supported with problem-relevant background knowledge extracted automatically and on demand from external sources like, e.g., WordNet, YAGO, and OpenCyc, or other, more experimental sources with, e.g., manually defined presupposition resolutions, or with axiomatized general and common sense knowledge. The results show that fine-grained and consistent knowledge coming from diverse sources is a necessary condition determining the correctness and traceability of results.Comment: 25 pages, 10 figure

A Satisfiability-based Approach for Generalized Tanglegrams on Level Graphs

A tanglegram is a pair of (not necessarily binary) trees on the same set of leaves with matching leaves in the two trees joined by an edge. Tanglegrams are widely used in computational biology to compare evolutionary histories of species. In this work we present a formulation of two related combinatorial embedding problems concerning tanglegrams in terms of CNF-formulas. The first problem is known as the planar embedding and the second as the crossing minimization problem. We show that our satisfiability-based encoding of these problems can handle a much more general case with more than two, not necessarily binary or complete, trees defined on arbitrary sets of leaves and allowed to vary their layouts. Furthermore, we present an experimental comparison of our technique and several known heuristics for solving generalized binary tanglegrams, showing its competitive performance and efficiency and thus proving its practical usability

Probabilistic Analysis of Random Mixed Horn Formulas

We present a probabilistic analysis of random mixed Horn formulas (MHF), i.e., formulas in conjunctive normal form consisting of a positive monotone part of quadratic clauses and a part of Horn clauses, with m clauses, n variables, and up to n literals per Horn clause. For MHFs parameterized by n and m with uniform distribution of instances and for large n, we derive upper bounds for the expected number of models. For the class of random negative MHFs, where only monotone negative Horn clauses are allowed to occur, we give a lower bound for the probability that formulas from this class are satisfiable. We expect that the model studied theoretically here may be of interest for the determination of hard instances, which are conjectured to be found in the transition area from satisfiability to unsatisfiability of the instances from the parameterized classes of formulas

Probabilistic Analysis of Random Mixed Horn Formulas

We present a probabilistic analysis of random mixed Horn formulas (MHF), i.e., formulas in conjunctive normal form consisting of a positive monotone part of quadratic clauses and a part of Horn clauses, with m clauses, n variables, and up to n literals per Horn clause. For MHFs parameterized by n and m with uniform distribution of instances and for large n, we derive upper bounds for the expected number of models. For the class of random negative MHFs, where only monotone negative Horn clauses are allowed to occur, we give a lower bound for the probability that formulas from this class are satisfiable. We expect that the model studied theoretically here may be of interest for the determination of hard instances, which are conjectured to be found in the transition area from satisfiability to unsatisfiability of the instances from the parameterized classes of formulas

XSAT and NAE-SAT of linear CNF classes

XSAT and NAE-SAT are important variants of the propositional satisfiability problem (SAT). Both are studied here regarding their computational complexity of linear CNF formulas. We prove that both variants remain NP-complete for (monotone) linear formulas yielding the conclusion that also bicolorability of linear hypergraphs is NP-complete. The reduction used gives rise to the complexity investigation of both variants for several monotone linear subclasses that are parameterized by the size of clauses or by the number of occurrences of variables. In particular cases of these parameter values we are able to verify the NP-completeness of XSAT respectively NAE-SAT; though we cannot provide a complete treatment. Finally we focus on exact linear formulas where clauses intersect pairwise, and for which SAT is known to be polynomial-time solvable. We verify the same assertion for NAE-SAT relying on some well-known result; whereas we obtain NP-completeness for XSAT of exact linear formulas. The case of uniform clause size k remains open for the latter problem. However, we can provide its polynomial-time behavior for k at most 6

Effectiveness of pre- and inprocessing for CDCL-based SAT solving

Applying pre- and inprocessing techniques to simplify CNF formulas both before and during search can considerably improve the performance of modern SAT solvers. These algorithms mostly aim at reducing the number of clauses, literals, and variables in the formula. However, to be worthwhile, it is necessary that their additional runtime does not exceed the runtime saved during the subsequent SAT solver execution. In this paper we investigate the efficiency and the practicability of selected simplification algorithms for CDCL-based SAT solving. We first analyze them by means of their expected impact on the CNF formula and SAT solving at all. While testing them on real-world and combinatorial SAT instances, we show which techniques and combinations of them yield a desirable speedup and which ones should be avoided

XSAT and NAE-SAT of linear CNF classes

XSAT and NAE-SAT are important variants of the propositional satisfiability problem (SAT). Both are studied here regarding their computational complexity of linear CNF formulas. We prove that both variants remain NP-complete for (monotone) linear formulas yielding the conclusion that also bicolorability of linear hypergraphs is NP-complete. The reduction used gives rise to the complexity investigation of both variants for several monotone linear subclasses that are parameterized by the size of clauses or by the number of occurrences of variables. In particular cases of these parameter values we are able to verify the NP-completeness of XSAT respectively NAE-SAT; though we cannot provide a complete treatment. Finally we focus on exact linear formulas where clauses intersect pairwise, and for which SAT is known to be polynomial-time solvable. We verify the same assertion for NAE-SAT relying on some well-known result; whereas we obtain NP-completeness for XSAT of exact linear formulas. The case of uniform clause size k remains open for the latter problem. However, we can provide its polynomial-time behavior for k at most 6