Efficient access mechanisms for tabled logic programs

Ramakrishnan, I. V.; Rao, Prasad; Sagonas, Konstantinos; Swift, Terrance; Warren, David S.

doi:10.1016/s0743-1066(98)10013-4

Cited by 88 publications

(80 citation statements)

References 10 publications

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“…Although Prolog users are used to this it is desirable to have more high-level query optimisation. Constraint logic programming (CLP) and tabling [10] possibly can improve efficiency and allow a more declarative programming style in the presence of cycles in relations and other abnormalities in the searchspace.…”

Section: Discussionmentioning

confidence: 99%

Prolog-Based Infrastructure for RDF: Scalability and Performance

Wielemaker

Schreiber

Wielinga

2003

Lecture Notes in Computer Science

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Abstract:The semantic web is a promising application-area for the Prolog programming language for its non-determinism and pattern-matching. In this paper we outline an infrastructure for loading and saving RDF/XML, storing triples, elementary reasoning with triples and visualization. A predecessor of the infrastructure described here has been used in various applications for ontology-based annotation of multimedia objects using semantic web languages. Our library aims at fast parsing, fast access and scalability for fairly large but not unbounded applications upto 40 million triples.The RDF parser is distributed with SWI-Prolog under the LGPL Free Software licence. The other components will be added to the distribution as they become stable and documented.

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Section: Discussionmentioning

confidence: 99%

Prolog-Based Infrastructure for RDF: Scalability and Performance

Wielemaker

Schreiber

Wielinga

2003

Lecture Notes in Computer Science

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“…an entry in a variant table is used to satisfy a subsequent goal if that goal is a variant of the goal that generated the table entry, i.e., equal up to change of variable. In subsumptive tabling (Johnson et al 1999;Ramakrishnan et al 1999;Johnson 2000;Johnson 2002;Cruz and Rocha 2011), a table entry is used to satisfy a subsequent goal if the subsequent goal is subsumed by the generating goal, i.e., is an instance of the generating goal. Note that in such a case the answers to the subsumed goal will (eventually) be in the table of answers for the previously encountered subsuming goal.…”

Section: Specifying Bottom-up Evaluation In Xsbmentioning

confidence: 99%

Top-down and Bottom-up Evaluation Procedurally Integrated

Warren

2018

EasyChair Preprints

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This paper describes how the Logic Programming System XSB combines top-down and bottomup computation through the mechanisms of variant tabling and subsumptive tabling with abstraction, respectively.It is well known that top-down evaluation of logical rules in Prolog has a procedural interpretation as recursive procedure invocation (Kowalski 1986). Tabling adds the intuition of short-circuiting redundant computations (Warren 1992). This paper shows how to introduce into tabled logic program evaluation a bottom-up component, whose procedural intuition is the initialization of a data structure, in which a relation is initially computed and filled, on first demand, and then used throughout the remainder of a larger computation for efficient lookup. This allows many Prolog programs to be expressed fully declaratively, programs which formerly required procedural features, such as assert, to be made efficient.

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“…This suggests using tries to maintain the set of proofs. We use the YAP implementation of tries for this task, based itself on XSB Prolog's work on tries of terms (Ramakrishnan et al 1999), which we briefly summarize here. Tries (Fredkin 1962) were originally invented to index dictionaries, and have since been generalised to index recursive data structures such as terms.…”

Section: Sets Of Proofsmentioning

confidence: 99%

On the implementation of the probabilistic logic programming language ProbLog

Kimmig¹,

Demoen²,

Raedt³

et al. 2011

Theory and Practice of Logic Programming

117

152

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The past few years have seen a surge of interest in the field of probabilistic logic learning and statistical relational learning. In this endeavor, many probabilistic logics have been developed. ProbLog is a recent probabilistic extension of Prolog motivated by the mining of large biological networks. In ProbLog, facts can be labeled with probabilities. These facts are treated as mutually independent random variables that indicate whether these facts belong to a randomly sampled program. Different kinds of queries can be posed to ProbLog programs. We introduce algorithms that allow the efficient execution of these queries, discuss their implementation on top of the YAP-Prolog system, and evaluate their performance in the context of large networks of biological entities.

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Efficient access mechanisms for tabled logic programs

Cited by 88 publications

References 10 publications

Prolog-Based Infrastructure for RDF: Scalability and Performance

Prolog-Based Infrastructure for RDF: Scalability and Performance

Top-down and Bottom-up Evaluation Procedurally Integrated

On the implementation of the probabilistic logic programming language ProbLog

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