The YAP Prolog system

Costa, Vítor Santos; Rocha, Ricardo; Damas, Lúıs

doi:10.1017/s1471068411000512

Cited by 100 publications

(81 citation statements)

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“…EYE (Euler YAP Engine) [13], [14], is a backward-chaining inference engine based on underlying Prolog YAP engine [15], a high-performance Prolog compiler for demand-driven indexing. The inference engine uses backward-forward-backward chaining reasoning cycle and Euler path detection algorithm to avoid loops in an inference graph.…”

Section: Comparison Of Rule-based Inference Enginesmentioning

confidence: 99%

A Comparative Study of Rule-Based Inference Engines for the Semantic Web

Rattanasawad

Buranarach

Saikaew

et al. 2018

IEICE Trans. Inf. & Syst.

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SUMMARYWith the Semantic Web data standards defined, more applications demand inference engines in providing support for intelligent processing of the Semantic Web data. Rule-based inference engines or rule-based reasoners are used in many domains, such as in clinical support, and e-commerce recommender system development. This article reviews and compares key features of three freely-available rule-based reasoners: Jena inference engine, Euler YAP Engine, and BaseVISor. A performance evaluation study was conducted to assess the scalability and efficiency of these systems using data and rule sets adapted from the Berlin SPARQL Benchmark. We describe our methodology in assessing rule-based reasoners based on the benchmark. The study result shows the efficiency of the systems in performing reasoning tasks over different data sizes and rules involving various rule properties. The review and comparison results can provide a basis for users in choosing appropriate rule-based inference engines to match their application requirements. key words: rule language, rule-based reasoner, benchmark

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Section: Comparison Of Rule-based Inference Enginesmentioning

confidence: 99%

A Comparative Study of Rule-Based Inference Engines for the Semantic Web

Rattanasawad

Buranarach

Saikaew

et al. 2018

IEICE Trans. Inf. & Syst.

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“…Várias plataformas PROLOG [23,7,13,26,34,43] disponibilizam essa funcionalidade devido tanto à popularização das máquinas paralelas quanto à disponibilização dessa funcionalidade em outras linguagens, como C e Java.…”

Section: Multithreadingunclassified

Paralelismo em Prolog: Conceitos e Sistemas

Filho

Silva²

2016

RITA

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Resumo: Paralelismo é uma área de estudo que cresce a cada dia, devido à redução do custo e popularização de máquinas com arquiteturas paralelas. Nesse contexto, as linguagens lógicas, sobretudo o PROLOG, apresenta uma alternativa viável e prá-tica de paralelismo. A exploração desse paralelismo pode ser realizada de diferentes formas, e há inúmeros desafios nessa tarefa. Este tutorial visa apresentar os principais conceitos de paralelismo em PROLOG, os desafios enfrentados quando se busca a paralelização nessa linguagem e o estado-da-arte do desenvolvimento de sistemas que dão suporte à paralelização em linguagens lógicas. São apresentados sistemas baseados em paralelismo implícito implementados em diferentes plataformas. Ao final é realizada uma comparação entre os sistemas apresentados e os modelos neles implementados.Palavras-chave: prolog, paralelismo, paralelismo-E, paralelismo-OU, parale- lismo implícitoAbstract: Parallelism is a study area that grows up each day, caused by the cost reduction and popularizing of machines with parallels architecture. In this context, the logical languages, especially PROLOG, show a feasible and practical alternative of parallelism. This exploitation can be accomplished of different ways, and are there several challenges on this task. This survey aims to show the main concepts of parallelism in PROLOG, the faced challenges when aims to do parallelism in this language and the state-of-art of systems development to give parallelism support in logical languages. Systems with basis on implicit parallelism developed in different platforms are presented. At the end, is accomplished a comparison between the presented systems and the implemented models by they.

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“…In our case we have been working with its Yap (Santos Costa et al, 2002) implementation ("yaleph"). After launching Aleph we have to load the learning input files (*.b and *.f) through the command "read_all(+BaseFileName)".…”

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confidence: 99%

Rule Induction for Sentence Reduction

Cordeiro

Dias

Brazdil

2013

Progress in Artificial Intelligence

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Covilhã, Novembro de 2010ii I would like to dedicate this thesis to my loving wife, Adelina Amorim, and my three precious children: David, Tiago, and André.iii iv AcknowledgementsA work like this is almost impossible to be achieved by a single individual working alone.There are always several very important persons involved, more or less directly and in various levels: scientific, personal, or even in the sentimental/spiritual level. In this few lines I would like to express my deepest gratitude to those having been determinate for the conclusion of this long and hard work.I would like to start by acknowledging my supervisor, Prof. Doctor Gaël Harry Dias, for his constant and relentless support throughout this journey. He was indeed an always presently supporter, guiding me many times, and even motivating me toward new unexplored scientific and technological territories. My co-supervisor, Professor Pavel Bernard Brazdil, was equally very important for the developing and conclusion of this work. With his long experience as a leading scientist, every piece of advise received from him were carefully observed and incorporated here. I also want to thank all the teachers I had, spe- AbstractThe field of Automatic Sentence Reduction has been an active research topic, with several relevant approaches being recently proposed. However, in our view many milestones still need to be reached in order to approach human-like quality sentence simplification.In this work, we propose a new framework, which processes huge sets of web news stories and learns sentence reduction rules in a fully automated and unsupervised way. This is our main contribution. Our system is conceptually composed of several modules. In the first one, the system automatically extracts paraphrases from on-line news stories, using new lexically based functions that we have proposed. In our system's second module, the extracted paraphrases are transformed into aligned paraphrases, meaning that the two paraphrasic sentences get their words aligned through DNA-like sequence alignment algorithms, that has been conveniently adapted for aligning sequences of words. These alignments are then explored and specific text structures called bubbles are selected.Afterwards, these structures are transformed into learning instances and used in the last learning module that exploits techniques of Inductive Logic Programming. This module learns the rules for sentence reduction. Results show that this is a good approach for learning automatic sentence reduction, while some pertinent issues still need future investigation. KeywordsSentence reduction, sentence compression, sentence simplification, paraphrase extraction, paraphrase alignment, automatic text summarization, natural language processing, inductive logic programing, machine learning, artificial intelligence. Palavras-chaveRedução de frases, compressão de frases, simplificação de frases, extracção de paráfrases, alinhamento de paráfrases, sumarização automática de texto, processamento da linguagem natural, programaç...

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The YAP Prolog system

Cited by 100 publications

References 46 publications

A Comparative Study of Rule-Based Inference Engines for the Semantic Web

A Comparative Study of Rule-Based Inference Engines for the Semantic Web

Paralelismo em Prolog: Conceitos e Sistemas

Rule Induction for Sentence Reduction

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