Two formalisms of extended possibilistic logic programming with context-dependent fuzzy unification A comparative description

AbstractPossibilistic logic is a logic of uncertainty where a certainty degree between 0 and 1, interpreted as a lower bound of a necessity measure, is attached to each classical formula. In this paper we present a comparative description of two models extending first order possibilistic logic so as to allow for fuzzy unification. The first formalism, called PLFC, is a general extension that allows clauses with fuzzy constants and fuzzily restricted quantifiers. The second formalism is an implication-based extension defined on top of Gödel infinitely-valued logic, capable of dealing with fuzzy constants. In this paper we compare these approaches, mainly their Horn-clause fragments, discussing their basic differences, specially in what regards their unification and automated deduction mechanisms

KOMET - A System for the Integration of Heterogeneous Information Sources

. We present KOMET, an architecture for the intelligent integration of heterogeneous information sources. It is based on the idea of a mediator, which is an independent software layer between an application and various knowledge sources which need to be accessed. We present an especially suitable logic-based language for encoding typical mediation tasks like conditional preference strategies, schema integration or data inconsistency resolution. Using annotated logic, KOMET is able to perform various common types of reasoning, such as probabilistic, fuzzy, paraconsistent and certain types of temporal and spatial reasoning. In combination with an extensible type system and the embedding of external knowledge sources as constraint domains, our mediation language offers a rich framework, which not only facilitates access to structured information, but as well supports unstructured and semi-structured information. A number of examples show the practical application of our approach. Keywords..