Four Lessons in Versatility or How Query Languages Adapt to the Web

Exposing not only human-centered information, but machine-processable data on the Web is one of the commonalities of recent Web trends. It has enabled a new kind of applications and businesses where the data is used in ways not foreseen by the data providers. Yet this exposition has fractured the Web into islands of data, each in different Web formats: Some providers choose XML, others RDF, again others JSON or OWL, for their data, even in similar domains. This fracturing stifles innovation as application builders have to cope not only with one Web stack (e.g., XML technology) but with several ones, each of considerable complexity. With Xcerpt we have developed a rule- and pattern based query language that aims to give shield application builders from much of this complexity: In a single query language XML and RDF data can be accessed, processed, combined, and re-published. Though the need for combined access to XML and RDF data has been recognized in previous work (including the W3C’s GRDDL), our approach differs in four main aspects: (1) We provide a single language (rather than two separate or embedded languages), thus minimizing the conceptual overhead of dealing with disparate data formats. (2) Both the declarative (logic-based) and the operational semantics are unified in that they apply for querying XML and RDF in the same way. (3) We show that the resulting query language can be implemented reusing traditional database technology, if desirable. Nevertheless, we also give a unified evaluation approach based on interval labelings of graphs that is at least as fast as existing approaches for tree-shaped XML data, yet provides linear time and space querying also for many RDF graphs. We believe that Web query languages are the right tool for declarative data access in Web applications and that Xcerpt is a significant step towards a more convenient, yet highly efficient data access in a “Web of Data”

LUPS - A language for updating logic programs

Most of the work conducted so far in the field of logic programming has focused on representing static knowledge, i.e. knowledge that does not evolve with time. To overcome this limitation, in a recent paper, the authors introduced the concept of dynamic logic programming. There, they studied and defined the declarative and operational semantics of sequences of logic programs (or dynamic logic programs), P-o + ... + P-n. Each such program contains knowledge about some given state, where different states may, e.g., represent different time periods or different sets of priorities. The role of dynamic logic programming is to employ relationships existing between the possibly mutually contradictory sequence of programs to precisely determine, at any given state, the declarative and procedural semantics of their combination. But how, in concrete situations, is a sequence of logic programs built? For instance, in the domain of actions, what are the appropriate sequences of programs that represent the performed actions and their effects? Whereas dynamic logic programming provides a way for determining what should follow, given the sequence of programs, it does not provide a good practical language for the specification of updates or changes in the knowledge represented by successive logic progams. In this paper we define a language designed for specifying changes to logic programs (LUPS - "Language for dynamic updates"). Given an initial knowledge base (in the form of a logic program) LUPS provides a way for sequentially updating it. The declarative meaning of a sequence of sets of update actions in LUPS is defined using the semantics of the dynamic logic program generated by those actions. We also provide a translation of the sequence of update statements sets into a single generalized logic program written in a meta-language, so that the stable models of the resulting program correspond to the previously defined declarative semantics. This meta-language is used in the actual implementation, although this is not the subject of this paper. Finally we briefly mention related work (lack of space prevents us from presenting more detailed comparisons).publishe

Updates of Logic Programs by Logic Programs

In this paper we investigate updates of knowledge bases represented by logic programs. In order to represent negative information, we use generalized logic programs which allow default negation not only in rule bodies but also in their heads.We start by introducing the notion of an update P \Phi U of a logic program P by another logic program U . Subsequently, we provide a precise semantic characterization of P \PhiU , and study some basic properties of program updates. In particular, we show that our update programs generalize the notion of interpretation update. We then extend this notion to compositional sequences of logic programs updates P1 \Phi P2 \Phi : : : , defining a dynamic program update, and thereby introducing the paradigm of dynamic logic programming. This paradigm significantly facilitates modularization of logic programming, and thus modularization of non-monotonic reasoning as a whole. Specifically, suppose that we are given a set of logic program modules, e..

Dynamic Logic Programming

In this paper we investigate updates of knowledge bases represented by logic programs. In order to represent negative information, we use generalized logic programs which allow default negation not only in rule bodies but also in their heads.We start by introducing the notion of an update P U of a logic program P by another logic program U. Subsequently, we provide a precise semantic characterization of P U, and study some basic properties of program updates. In particular, we show that our update programs generalize the notion of interpretation update. We then extend this notion to compositional sequences of logic programs updates P1P2 ::: , dening a dynamic program update, and thereby introducing the paradigm of dynamic logic programming. This paradigm significantly facilitates modularization of logic programming, and thus modularization of nonmonotonic reasoning as a whole. Specically, suppose that we are given a set of logic program modules, each describing a dierent state of our knowledge of the world. Dierent states may represent dierent time points or dierent sets of priorities or perhaps even dierent viewpoints. Consequently, program modules may contain mutually contradictory as well as overlapping information. The role of the dynamic program update is to employ the mutual relationships existing between dierent modules to precisely determine, at any given module composition stage, the declarative as well as the procedural semantics of the combined program resulting from the modules.authorsversionpublishe

Dynamic Logic Programming

In this paper we investigate updates of knowledge bases represented by logic programs. In order to represent negative information, we use generalized logic programs which allow default negation not only in their bodies but also in their heads.We start by introducing the notion of an update P \Phi U of a logic program P by another logic program U . Subsequently, we provide a precise semantic characterization of P \Phi U , and study some basic properties of program updates. In particular, we show that our update programs generalize the notion of interpretation update. We then extend this notion to sequences of logic programs updates P1 \Phi P2 \Phi : : : , defining dynamic program updates, thereby introducing the paradigm of dynamic logic programming. This paradigm significantly facilitates modularization of logic programming, and thus modularization of non-monotonic reasoning as a whole. Specifically, suppose that we are given a set of logic program modules, each describing a different st..