Reordering Query and Rule Patterns for Query Answering in a Rete-Based Inference Engine

2005 ◽  
pp. 255-265 ◽  
Author(s):  
Murat Osman Ünalır ◽  
Tuğba Özacar ◽  
Övünç Öztürk
Keyword(s):  
Inference Engine ◽  
Query Answering

Computing Cores for Existential Rules with the Standard Chase and ASP

2020 ◽  
Author(s):  
Markus Krötzsch
Keyword(s):  
Large Class ◽  
Data Exchange ◽  
Answer Set Programming ◽  
Query Answering ◽  
Universal Model ◽  
The Core ◽  
Universal Models ◽  
General Terms ◽  
Special Cases ◽  
The Many

To reason with existential rules (a.k.a. tuple-generating dependencies), one often computes universal models. Among the many such models of different structure and cardinality, the core is arguably the “best”. Especially for finitely satisfiable theories, where the core is the unique smallest universal model, it has advantages in query answering, non-monotonic reasoning, and data exchange. Unfortunately, computing cores is difficult and not supported by most reasoners. We therefore propose ways of computing cores using practically implemented methods from rule reasoning and answer set programming. Our focus is on cases where the standard chase algorithm produces a core. We characterise this desirable situation in general terms that apply to a large class of cores, derive concrete approaches for decidable special cases, and generalise these approaches to non-monotonic extensions of existential rules.

Corrigendum to WebPIE: A Web-Scale Parallel Inference Engine Using MapReduce

2012 ◽  
Author(s):  
Jacopo Urbani ◽  
Spyros Kotoulas ◽  
Jason Massen ◽  
Frank van Harmelen ◽  
Henri Bal
Keyword(s):  
Inference Engine ◽  
Parallel Inference

An Improved Set-based Reasoner for the Description Logic 𝒟ℒD4,׆

2021 ◽  
Vol 178 (4) ◽  
pp. 315-346
Author(s):  
Domenico Cantone ◽  
Marianna Nicolosi-Asmundo ◽  
Daniele Francesco Santamaria
Keyword(s):  
Semantic Web ◽  
Set Theory ◽  
Description Logic ◽  
Knowledge Bases ◽  
Query Answering ◽  
Conjunctive Query ◽  
Classification Problems ◽  
Previous Version ◽  
Decidable Fragment ◽  
Stratified Set

We present a KE-tableau-based implementation of a reasoner for a decidable fragment of (stratified) set theory expressing the description logic 𝒟ℒ〈4LQSR,×〉(D) (𝒟ℒD4,×, for short). Our application solves the main TBox and ABox reasoning problems for 𝒟ℒD4,×. In particular, it solves the consistency and the classification problems for 𝒟ℒD4,×-knowledge bases represented in set-theoretic terms, and a generalization of the Conjunctive Query Answering problem in which conjunctive queries with variables of three sorts are admitted. The reasoner, which extends and improves a previous version, is implemented in C++. It supports 𝒟ℒD4,×-knowledge bases serialized in the OWL/XML format and it admits also rules expressed in SWRL (Semantic Web Rule Language).

scholarly journals Introducing the StataStan Interface for Fast, Complex Bayesian Modeling Using Stan

2017 ◽  
Vol 17 (2) ◽  
pp. 330-342 ◽  
Author(s):  
Robert L. Grant ◽  
Bob Carpenter ◽  
Daniel C. Furr ◽  
Andrew Gelman
Keyword(s):  
Bayesian Inference ◽  
Item Response Theory ◽  
Open Source ◽  
Item Response ◽  
Bayesian Modeling ◽  
Inference Engine ◽  
Software Components ◽  
Statistical Software ◽  
Simulation Based ◽  
Item Response Theory Models

In this article, we present StataStan, an interface that allows simulation-based Bayesian inference in Stata via calls to Stan, the flexible, open-source Bayesian inference engine. Stan is written in C++, and Stata users can use the commands stan and windowsmonitor to run Stan programs from within Stata. We provide a brief overview of Bayesian algorithms, details of the commands available from Statistical Software Components, considerations for users who are new to Stan, and a simple example. Stan uses a different algorithm than bayesmh, BUGS, JAGS, SAS, and MLwiN. This algorithm provides considerable improvements in efficiency and speed. In a companion article, we give an extended comparison of StataStan and bayesmh in the context of item response theory models.

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