Invited Lectures.- Tableau Algorithms for Description Logics.- Modality and Databases.- Local Symmetries in Propositional Logic.- Comparison.- Design and Results of TANCS-2000 Non-classical (Modal) Systems Comparison.- Consistency Testing: The RACE Experience.- Benchmark Analysis with FaCT.- MSPASS: Modal Reasoning by Translation and First-Order Resolution.- TANCS-2000 Results for DLP.- Evaluating *SAT on TANCS 2000 Benchmarks.- Research Papers.- A Labelled Tableau Calculus for Nonmonotonic (Cumulative) Consequence Relations.- A Tableau System for Godel-Dummett Logic Based on a Hypersequent Calculus.- An Analytic Calculus for Quantified Propositional Godel Logic.- A Tableau Method for Inconsistency-Adaptive Logics.- A Tableau Calculus for Integrating First-Order and Elementary Set Theory Reasoning.- Hypertableau and Path-Hypertableau Calculi for some Families of Intermediate Logics.- Variants of First-Order Modal Logics.- Complexity of Simple Dependent Bimodal Logics.- Properties of Embeddings from Int to S4.- Term-Modal Logics.- A Subset-Matching Size-Bounded Cache for Satisfiability in Modal Logics.- Dual Intuitionistic Logic Revisited.- Model Sets in a Nonconstructive Logic of Partial Terms with Definite Descriptions.- Search Space Compression in Connection Tableau Calculi Using Disjunctive Constraints.- Matrix-Based Inductive Theorem Proving.- Monotonic Preorders for Free Variable Tableaux.- The Mosaic Method for Temporal Logics.- Sequent-Like Tableau Systems with the Analytic Superformula Property for the Modal Logics KB, KDB, K5, KD5.- A Tableau Calculus for Equilibrium Entailment.- Towards Tableau-Based Decision Procedures for Non-Well-Founded Fragments of Set Theory.- Tableau Calculus for Only Knowing and Knowing At Most.- A Tableau-Like Representation Framework for Efficient Proof Reconstruction.- The Semantic Tableaux Version of the Second Incompleteness Theorem Extends Almost to Robinson's Arithmetic Q.- System Descriptions.- Redundancy-Free Lemmatization in the Automated Model-Elimination Theorem Prover AI-SETHEO.- E-SETHEO: An Automated3 Theorem Prover.

Automated Reasoning with Analytic Tableaux and Related Methods: International Conference, TABLEAUX 2000 St Andrews, Scotland, UK, July 3-7, 2000 Proceedings

We study the query answering using views (QAV) problem for tree pattern queries. Given a query and a view, the QAV problem is traditionally formulated in two ways: (i) find an equivalent rewriting of the query using only the view, or (ii) find a maximal contained rewriting using only the view. The former is appropriate for classical query optimization and was recently studied by Xu and Ozsoyoglu for tree pattern queries (TP). However, for information integration, we cannot rely on equivalent rewriting and must instead use maximal contained rewriting as shown by Halevy. Motivated by this, we study maximal contained rewriting for TP, a core subset of XPath, both in the absence and presence of a schema. In the absence of a schema, we show there are queries whose maximal contained rewriting (MCR) can only be expressed as the union of exponentially many TPs. We characterize the existence of a maximal contained rewriting and give a polynomial time algorithm for testing the existence of an MCR. We also give an algorithm for generating the MCR when one exists. We then consider QAV in the presence of a schema. We characterize the existence of a maximal contained rewriting when the schema contains no recursion or union types, and show that it consists of at most one TP. We give an efficient polynomial time algorithm for generating the maximal contained rewriting whenever it exists. Finally, we discuss QAV in the presence of recursive schemas.

/pdf/answering-tree-pattern-queries-using-views-xbvgrvmt4b.pdf

Answering tree pattern queries using views

That one-quarter of Medicare spending in the United States occurs in the last year of life is commonly interpreted as waste. But this interpretation presumes knowledge of who will die and when. Here we analyze how spending is distributed by predicted mortality, based on a machine-learning model of annual mortality risk built using Medicare claims. Death is highly unpredictable. Less than 5% of spending is accounted for by individuals with predicted mortality above 50%. The simple fact that we spend more on the sick—both on those who recover and those who die—accounts for 30 to 50% of the concentration of spending on the dead. Our results suggest that spending on the ex post dead does not necessarily mean that we spend on the ex ante “hopeless.”

/pdf/predictive-modeling-of-u-s-health-care-spending-in-late-life-2787a7zajb.pdf

Predictive modeling of U.S. health care spending in late life.

Separation logic (SL) is a widely used formalism for verifying heap manipulating programs. Existing SL solvers focus on decidable fragments for list-like structures. More complex data structures such as trees are typically unsupported in implementations, or handled by incomplete heuristics. While complete decision procedures for reasoning about trees have been proposed, these procedures suffer from high complexity, or make global assumptions about the heap that contradict the separation logic philosophy of local reasoning. In this paper, we present a fragment of classical first-order logic for local reasoning about tree-like data structures. The logic is decidable in NP and the decision procedure allows for combinations with other decidable first-order theories for reasoning about data. Such extensions are essential for proving functional correctness properties. We have implemented our decision procedure and, building on earlier work on translating SL proof obligations into classical logic, integrated it into an SL-based verification tool. We successfully used the tool to verify functional correctness of tree-based data structure implementations.

/pdf/automating-separation-logic-with-trees-and-data-1zy24sagtj.pdf

Automating Separation Logic with Trees and Data

The discrete cosine transform (DCT), introduced by Ahmed, Natarajan and Rao, has been used in many applications of digital signal processing, data compression and information hiding. There are four types of the discrete cosine transform. In simulating the discrete cosine transform, we propose a generalized discrete cosine transform with three parameters, and prove its orthogonality for some new cases. A new type of discrete cosine transform is proposed and its orthogonality is proved. Finally, we propose a generalized discrete W transform with three parameters, and prove its orthogonality for some new cases. Keywords: Discrete Fourier transform, discrete sine transform, discrete cosine transform, discrete W transform Nigerian Journal of Technological Research , vol7(1) 2012

On discrete cosine transform

When searching for flights, current systems often suggest routes
involving waiting times at stopovers. There might exist alternative routes which are
more attractive from a touristic perspective because their duration is
not necessarily much longer while offering enough time in an
appropriate place. Choosing among such
alternatives requires additional planning efforts to make sure that
e.g. points of interest can conveniently be reached in the
allowed time frame. We present a system that automatically computes smart trip
alternatives between any two cities. To do so, it searches
points of interest in large semantic datasets considering the
set of accessible areas around each possible layover. It then elects feasible alternatives and displays their
differences with respect to the default trip.

/pdf/smart-trip-alternatives-for-the-curious-2b8xztnrav.pdf

Smart Trip Alternatives for the Curious

Graph data is omnipresent and has a wide variety of applications, such as in natural science, social networks, or the semantic web. However, while being rich in information, graphs are often noisy and incomplete. As a result, graph completion tasks, such as node classification or link prediction, have gained attention. On one hand, neural methods, such as graph neural networks, have proven to be robust tools for learning rich representations of noisy graphs. On the other hand, symbolic methods enable exact reasoning on graphs. We propose Knowledge Enhanced Graph Neural Networks (KeGNN), a neuro-symbolic framework for graph completion that combines both paradigms as it allows for the integration of prior knowledge into a graph neural network model. Essentially, KeGNN consists of a graph neural network as a base upon which knowledge enhancement layers are stacked with the goal of refining predictions with respect to prior knowledge. We instantiate KeGNN in conjunction with two state-of-the-art graph neural networks, Graph Convolutional Networks and Graph Attention Networks, and evaluate KeGNN on multiple benchmark datasets for node classification.

Knowledge Enhanced Graph Neural Networks

We consider the optimization of recursive queries over graphs, and more specifically t he p roblem o f e fficiently enumerating recursive query plans. For a given query, the enumeration of logical plans – and in particular join enumeration – is an ex-tensively researched topic. However, query operators such as union and recursion have been largely neglected in enumeration techniques. We propose an extension of the logical query DAG (LQDAG) used in modern query optimizers with the support of recursion, and develop appropriate techniques to enumerate recursive terms efficiently. Ou r LQ DAG extension is based on the fixpoint o perator r ecently i ntroduced i n the 𝜇 -relational algebra. We show how to adapt transformation rules for individual fixpoint terms so that they can apply on a group of terms at once. The main benefit i s t o e nable a much more efficient ex ploration of re cursive qu ery pl an sp aces. We report on practical experiments with graph queries over real and synthetic graphs of varying size. Experimental results il-lustrate the benefits of exploring faster larger query execution plan spaces.

Optimizing Enumeration of Recursive Plans in Transformation-based Query Optimizers

The problem of SPARQL query containment has recently attracted a lot of attention due to its fundamental purpose in query optimization and information integration. New approaches to this problem, have been put forth, that can be implemented in practice. However, these approaches suffer from various limitations: coverage (size and type of queries), response time (how long it takes to determine containment), and the technique applied to encode the problem. In order to experimentally assess implementation limitations, we designed a benchmark suite offering different experimental settings depending on the type of queries, projection and reasoning (RDFS). We have applied this benchmark to three available systems using different techniques highlighting the strengths and weaknesses of such systems.

https://hal.inria.fr/hal-00749286/document

A Benchmark for Semantic Web Query Containment, Equivalence and Satisfiability

One major concept in web development using XML is validation: checking whether some document instance fulfills structural constraints described by some schema. Over the last few years, there has been a growing debate about XML validation, and two main schools of thought emerged about the way it should be done. On the one hand, some advocate the use of validation with respect to complete grammar-based descriptions such as DTDs and XML Schemas. On the other hand, motivated by a need for greater flexibility, others argue for no validation at all, or prefer the use of lightweight constraint languages such as Schematron with the aim of validating only required constraints, while making schema descriptions more compositional and more reusable.Owing to a logical compilation, we show that validators used in each of these approaches share the same theoretical foundations, meaning that the two approaches are far from being incompatible. Our findings include that the logic in [2] can be seen as a unifying formal ground for the construction of robust and efficient validators and static analyzers using any of these schema description techniques. This reconciles the two approaches from both a theoretical and a practical perspective, therefore facilitating any combination of them.

https://hal.inria.fr/hal-00837765/document

Pierre Genevès

Papers

Smart Trip Alternatives for the Curious

Knowledge Enhanced Graph Neural Networks

Optimizing Enumeration of Recursive Plans in Transformation-based Query Optimizers

A Benchmark for Semantic Web Query Containment, Equivalence and Satisfiability

XML validation: looking backward - strongly typed and flexible XML processing are not incompatible