Paraconsistent logic

About: Paraconsistent logic is a research topic. Over the lifetime, 1610 publications have been published within this topic receiving 28842 citations.

Automatic Indexing and Information Visualization: A Study Based on Paraconsistent Logic

Abstract: This paper reports a research to evaluate the potential and the effects of use of annotated Paraconsistent logic in automatic indexing. This logic attempts to deal with contradictions, concerned with studying and developing inconsistency-tolerant systems of logic. This logic, being flexible and containing logical states that go beyond the dichotomies yes and no, permits to advance the hypothesis that the results of indexing could be better than those obtained by traditional methods. Interactions between different disciplines, as information retrieval, automatic indexing, information visualization, and nonclassical logics were considered in this research. From the methodological point of view, an algorithm for treatment of uncertainty and imprecision, developed under the Paraconsistent logic, was used to modify the values of the weights assigned to indexing terms of the text collections. The tests were performed on an information visualization system named Projection Explorer (PEx), created at Institute of Mathematics and Computer Science (ICMC – USP São Carlos), with available source code. PEx uses traditional vector space model to represent documents of a collection. The results were evaluated by criteria built in the information visualization system itself, and demonstrated measurable gains in the quality of the displays, confirming the hypothesis that the use of the para-analyser under the conditions of the experiment has the ability to generate more effective clusters of similar documents. This is a point that draws attention, since the constitution of more significant clusters can be used to enhance information indexing and retrieval. It can be argued that the adoption of non-dichotomous (non-exclusive) parameters provides new possibilities to relate similar information.

A method for Handling Inconsistencies in Rule-based Classifiers

Abstract: Researches on distributed data mining have as the main interest the development of algorithms and approaches that make possible the analysis of large and physically distributed datasets proposing better solutions in terms of costs and computational complexity. In such a scenario, the handling of inconsistent classification rules, generated from distributed data sets is an important task, because inconsistencies can compromise the performance of the classifiers. The aim of this work is the development of a new methodology for knowledge integration based on paraconsistent logic. The method is capable to take reliable decisions in occurrences of inconsistent rules generated from a set of rule-based classifiers. The advantage of the proposed method is to avoid high flow of messages and data between distributed processors throughout the rules analysis generated from distributed data sets, because only local information is used for the generation of a global ruleset. Then, paraconsistent logic is used as a certainty management approach to infer the class for a test example. We could observe in the experiments done over different datasets that the method generates quite good results.

Paraconsistency, resolution and relevance.

Abstract: Digraphs provide an alternative syntax for propositional logic, with digraph kernels corresponding to classical models. Semikernels generalize kernels and we identify a subset of well-behaved semikernels that provides nontrivial models for inconsistent theories, specializing to the classical semantics for the consistent ones. Direct (instead of refutational) reasoning with classical resolution is sound and complete for this semantics, when augmented with a specific weakening which, in particular, excludes Ex Falso. Dropping all forms of weakening yields reasoning which also avoids typical fallacies of relevance.