We define XPathLog as a Datalog-style extension of XPath. XPathLog provides a clear, declarative language for querying and manipulating XML whose perspectives are especially in XML data integration. In our characterization, the formal semantics is defined wrt. an edge-labeled graph-based model, which covers the XML data model. We give a complete, logic-based characterization of XML data and the main language concept for XML, XPath. XPath-Logic extends the XPath language with variable bindings and embeds it into first-order logic. XPathLog is then the Horn fragment of XPath-Logic, providing a Datalog-style, rule-based language for querying and manipulating XML data. The model-theoretic semantics of XPath-Logic serves as the base of XPathLog as a logic-programming language, whereas also an equivalent answer-set semantics for evaluating XPathLog queries is given. In contrast to other approaches, the XPath syntax and semantics is also used for a declarative specification how the database should be updated: when used in rule heads, XPath filters are interpreted as specifications of elements and properties which should be added to the database.

It is well known that freeness and linearity information positively interact with aliasing information, allowing both the precision and the efficiency of the sharing analysis of logic programs to be improved. In this paper, we present a novel combination of set-sharing with freeness and linearity information, which is characterized by an improved abstract unification operator. We provide a new abstraction function and prove the correctness of the analysis for both the finite tree and the rational tree cases. Moreover, we show that the same notion of redundant information as identified in Bagnara et al. (2000) and Zaffanella et al. (2002) also applies to this abstract domain combination: this allows for the implementation of an abstract unification operator running in polynomial time and achieving the same precision on all the considered observable properties.

We present some applications of intermediate logics in the field of Answer Set Programming (ASP). A brief, but comprehensive introduction to the answer set semantics, intuitionistic and other intermediate logics is given. Some equivalence notions and their applications are discussed. Some results on intermediate logics are shown, and applied later to prove properties of answer sets. A characterization of answer sets for logic programs with nested expressions is provided in terms of intuitionistic provability, generalizing a recent result given by Pearce. It is known that the answer set semantics for logic programs with nested expressions may select non-minimal models. Minimal models can be very important in some applications, therefore we studied them; in particular we obtain a characterization, in terms of intuitionistic logic, of answer sets which are also minimal models. We show that the logic G3 characterizes the notion of strong equivalence between programs under the semantic induced by these models. Finally we discuss possible applications and consequences of our results. They clearly state interesting links between ASP and intermediate logics, which might bring research in these two areas together.

Binary logic programs can be obtained from ordinary logic programs by a binarizing transformation. In most cases, binary programs obtained this way are less efficient than the original programs. (Demoen, 1992) showed an interesting example of a logic program whose computational behaviour was improved when it was transformed to a binary program and then specialized by partial deduction. The class of B-stratifiable logic programs is defined. It is shown that for every B-stratifiable logic program, binarization and subsequent partial deduction produce a binary program which does not contain variables for continuations introduced by binarization. Such programs usually have a better computational behaviour than the original ones. Both binarization and partial deduction can be easily automated. A comparison with other related approaches to program transformation is given.

This paper illustrates how a Prolog program, using chronological backtracking to find a solution in some search space, can be enhanced to perform intelligent backtracking. The enhancement crucially relies on the impurity of Prolog that allows a program to store information when a dead end is reached. To illustrate the technique, a simple search program is enhanced.