Disjunctive deductive databases.

by Hwang Hoi Yee Cothan.Thesis (M.Phil.)--Chinese University of Hong Kong, 1996.Includes bibliographical references (leaves 68-70).Abstract --- p.iiAcknowledgement --- p.iiiChapter 1 --- Introduction --- p.1Chapter 1.1 --- Objectives of the Thesis --- p.1Chapter 1.2 --- Overview of the Thesis --- p.7Chapter 2 --- Background and Related Work --- p.8Chapter 2.1 --- Deductive Databases --- p.8Chapter 2.2 --- Disjunctive Deductive Databases --- p.10Chapter 2.3 --- Model tree for disjunctive deductive databases --- p.11Chapter 3 --- Preliminary --- p.13Chapter 3.1 --- Disjunctive Logic Program --- p.13Chapter 3.2 --- Data-disjunctive Logic Program --- p.14Chapter 4 --- Semantics of Data-disjunctive Logic Program --- p.17Chapter 4.1 --- Model-theoretic semantics --- p.17Chapter 4.2 --- Fixpoint semantics --- p.20Chapter 4.2.1 --- Fixpoint operators corresponding to the MMSpDD --- p.22Chapter 4.2.2 --- "Fixpoint operator corresponding to the contingency model, CMP" --- p.25Chapter 4.3 --- Equivalence between the model-theoretic and fixpoint semantics --- p.26Chapter 4.4 --- Operational Semantics --- p.30Chapter 4.5 --- Correspondence with the I-table --- p.31Chapter 5 --- Disjunctive Deductive Databases --- p.33Chapter 5.1 --- Disjunctions in deductive databases --- p.33Chapter 5.2 --- Relation between predicates --- p.35Chapter 5.3 --- Transformation of Disjunctive Deductive Data-bases --- p.38Chapter 5.4 --- Query answering for Disjunctive Deductive Data-bases --- p.40Chapter 6 --- Magic for Data-disjunctive Deductive Database --- p.44Chapter 6.1 --- Magic for Relevant Answer Set --- p.44Chapter 6.1.1 --- Rule rewriting algorithm --- p.46Chapter 6.1.2 --- Bottom-up evaluation --- p.49Chapter 6.1.3 --- Examples --- p.49Chapter 6.1.4 --- Discussion on the rewriting algorithm --- p.52Chapter 6.2 --- Alternative algorithm for Traditional Answer Set --- p.54Chapter 6.2.1 --- Rule rewriting algorithm --- p.54Chapter 6.2.2 --- Examples --- p.55Chapter 6.3 --- Contingency answer set --- p.56Chapter 7 --- Experiments and Comparison --- p.57Chapter 7.1 --- Experimental Results --- p.57Chapter 7.1.1 --- Results for the Traditional answer set --- p.58Chapter 7.1.2 --- Results for the Relevant answer set --- p.61Chapter 7.2 --- Comparison with the evaluation method for Model tree --- p.63Chapter 8 --- Conclusions and Future Work --- p.66Bibliography --- p.6

Computing only minimal answers in disjunctive deductive databases

A method is presented for computing minimal answers in disjunctive deductive databases under the disjunctive stable model semantics. Such answers are constructed by repeatedly extending partial answers. Our method is complete (in that every minimal answer can be computed) and does not admit redundancy (in the sense that every partial answer generated can be extended to a minimal answer), whence no non-minimal answer is generated. For stratified databases, the method does not (necessarily) require the computation of models of the database in their entirety. Compilation is proposed as a tool by which problems relating to computational efficiency and the non-existence of disjunctive stable models can be overcome. The extension of our method to other semantics is also considered.Comment: 48 page

A New Rational Algorithm for View Updating in Relational Databases

The dynamics of belief and knowledge is one of the major components of any autonomous system that should be able to incorporate new pieces of information. In order to apply the rationality result of belief dynamics theory to various practical problems, it should be generalized in two respects: first it should allow a certain part of belief to be declared as immutable; and second, the belief state need not be deductively closed. Such a generalization of belief dynamics, referred to as base dynamics, is presented in this paper, along with the concept of a generalized revision algorithm for knowledge bases (Horn or Horn logic with stratified negation). We show that knowledge base dynamics has an interesting connection with kernel change via hitting set and abduction. In this paper, we show how techniques from disjunctive logic programming can be used for efficient (deductive) database updates. The key idea is to transform the given database together with the update request into a disjunctive (datalog) logic program and apply disjunctive techniques (such as minimal model reasoning) to solve the original update problem. The approach extends and integrates standard techniques for efficient query answering and integrity checking. The generation of a hitting set is carried out through a hyper tableaux calculus and magic set that is focused on the goal of minimality.Comment: arXiv admin note: substantial text overlap with arXiv:1301.515

The DLV System for Knowledge Representation and Reasoning

This paper presents the DLV system, which is widely considered the state-of-the-art implementation of disjunctive logic programming, and addresses several aspects. As for problem solving, we provide a formal definition of its kernel language, function-free disjunctive logic programs (also known as disjunctive datalog), extended by weak constraints, which are a powerful tool to express optimization problems. We then illustrate the usage of DLV as a tool for knowledge representation and reasoning, describing a new declarative programming methodology which allows one to encode complex problems (up to $\Delta^P_3$-complete problems) in a declarative fashion. On the foundational side, we provide a detailed analysis of the computational complexity of the language of DLV, and by deriving new complexity results we chart a complete picture of the complexity of this language and important fragments thereof. Furthermore, we illustrate the general architecture of the DLV system which has been influenced by these results. As for applications, we overview application front-ends which have been developed on top of DLV to solve specific knowledge representation tasks, and we briefly describe the main international projects investigating the potential of the system for industrial exploitation. Finally, we report about thorough experimentation and benchmarking, which has been carried out to assess the efficiency of the system. The experimental results confirm the solidity of DLV and highlight its potential for emerging application areas like knowledge management and information integration.Comment: 56 pages, 9 figures, 6 table