37 research outputs found
A Polyvariant Binding-Time Analysis for Off-line Partial Deduction
We study the notion of binding-time analysis for logic programs. We formalise
the unfolding aspect of an on-line partial deduction system as a Prolog
program. Using abstract interpretation, we collect information about the
run-time behaviour of the program. We use this information to make the control
decisions about the unfolding at analysis time and to turn the on-line system
into an off-line system. We report on some initial experiments.Comment: 19 pages (including appendix) Paper (without appendix) appeared in
Programming Languages and Systems, Proceedings of the European Symposium on
Programming (ESOP'98), Part of ETAPS'98 (Chris Hankin, eds.), LNCS, vol.
1381, 1998, pp. 27-4
Experiences with Some Benchmarks for Deductive Databases and Implementations of Bottom-Up Evaluation
OpenRuleBench is a large benchmark suite for rule engines, which includes
deductive databases. We previously proposed a translation of Datalog to C++
based on a method that "pushes" derived tuples immediately to places where they
are used. In this paper, we report performance results of various
implementation variants of this method compared to XSB, YAP and DLV. We study
only a fraction of the OpenRuleBench problems, but we give a quite detailed
analysis of each such task and the factors which influence performance. The
results not only show the potential of our method and implementation approach,
but could be valuable for anybody implementing systems which should be able to
execute tasks of the discussed types.Comment: In Proceedings WLP'15/'16/WFLP'16, arXiv:1701.0014
Knowledge Rich Natural Language Queries over Structured Biological Databases
Increasingly, keyword, natural language and NoSQL queries are being used for
information retrieval from traditional as well as non-traditional databases
such as web, document, image, GIS, legal, and health databases. While their
popularity are undeniable for obvious reasons, their engineering is far from
simple. In most part, semantics and intent preserving mapping of a well
understood natural language query expressed over a structured database schema
to a structured query language is still a difficult task, and research to tame
the complexity is intense. In this paper, we propose a multi-level
knowledge-based middleware to facilitate such mappings that separate the
conceptual level from the physical level. We augment these multi-level
abstractions with a concept reasoner and a query strategy engine to dynamically
link arbitrary natural language querying to well defined structured queries. We
demonstrate the feasibility of our approach by presenting a Datalog based
prototype system, called BioSmart, that can compute responses to arbitrary
natural language queries over arbitrary databases once a syntactic
classification of the natural language query is made
Taking I/O seriously: resolution reconsidered for disk
Journal ArticleModern compilation techniques can give Prolog programs, in the best cases, a speed comparable to C. However, Prolog has proven to be unacceptable for data-oriented queries for two major reasons: its poor termination and complexity properties for Datalog, and its tuple-at-a-time strategy. A number of tabling frameworks and systems have addressed the first problem, including the XSB system which has achieved Prolog speeds for tabled programs. Yet tabling systems such as XSB continue to use the tuple-at-a-time paradigm. As a result, these systems are not amenable to a tight interconnection with disk-resident data. However, in a tabling framework the difference between tuple-at-a-time behavior and set-at-a-time can be viewed as one of scheduling. Accordingly, we define a breadth-first set-at-a-time tabling strategy and prove it iteration equivalent to a form of semi-naive magic evaluation. That is, we extend the well-known asymptotic results of Seki [10] by proving that each iteration of the tabling strategy produces the same information as semi-naive magic. Further, this set-at-a-time scheduling is amenable to implementation in an engine that uses Prolog compilation. We describe both the engine and its performance, which is comparable with the tuple-at-a-time strategy even for in-memory Datalog queries. Because of its performance and its fine level of integration of Prolog with a database-style search, the set-at-a-time engine appears as an important key to linking logic programming and deductive databases
Datalog: Bases de datos Deductivas
Este art铆culo muestra un breve estudio sobre Datalog, el cual es considerado como una聽 extensi贸n de Prolog que es uno de los sofware m谩s utilizados en la inteligencia artificial. Sistemas importantes como SWI-Prolog[22], Ciao Prolog[4], Sictus-Prolog[20], han sido compiladores utilizados para hacer uso de la funcionalidad del lenguaje l贸gico para bases de datos deductivas y han logrado la implementaci贸n de consultas recursivas聽 sobre las bases de datos relacionales. En este estudio tambi茅n se presentan conceptos b谩sicos de Datalog, as铆 como algunos sistemas que se han desarrollado para trabajar con este lenguaje.Palabra(s) Clave(s): Programaci贸n l贸gica, Prolog, Datalog
Linear Tabulated Resolution Based on Prolog Control Strategy
Infinite loops and redundant computations are long recognized open problems
in Prolog. Two ways have been explored to resolve these problems: loop checking
and tabling. Loop checking can cut infinite loops, but it cannot be both sound
and complete even for function-free logic programs. Tabling seems to be an
effective way to resolve infinite loops and redundant computations. However,
existing tabulated resolutions, such as OLDT-resolution, SLG- resolution, and
Tabulated SLS-resolution, are non-linear because they rely on the
solution-lookup mode in formulating tabling. The principal disadvantage of
non-linear resolutions is that they cannot be implemented using a simple
stack-based memory structure like that in Prolog. Moreover, some strictly
sequential operators such as cuts may not be handled as easily as in Prolog.
In this paper, we propose a hybrid method to resolve infinite loops and
redundant computations. We combine the ideas of loop checking and tabling to
establish a linear tabulated resolution called TP-resolution. TP-resolution has
two distinctive features: (1) It makes linear tabulated derivations in the same
way as Prolog except that infinite loops are broken and redundant computations
are reduced. It handles cuts as effectively as Prolog. (2) It is sound and
complete for positive logic programs with the bounded-term-size property. The
underlying algorithm can be implemented by an extension to any existing Prolog
abstract machines such as WAM or ATOAM.Comment: To appear as the first accepted paper in Theory and Practice of Logic
Programming (http://www.cwi.nl/projects/alp/TPLP