Programs for Information Integration in Multi-Valued Logics

Title: Programs for Information Integration in Multi-Valued Logics

Abstract: This research explores the problem of integrating information from various sources. The information consists of facts collected by a central server using logical rules and a hypothesis representing the server's own estimates. The study employs bilattices, like Belnap's four-valued logics, to create a formal framework for information integration. This framework includes a class of programs called Fitting, and a theory for information integration. The research also establishes an intuitive connection between the hypothesis testing mechanism and well-founded semantics and Kripke-Kleene semantics of Datalog programs with negation.

Keywords: Deductive databases, knowledge bases, information integration, logics of knowledge, inconsistency, bilattices

Main Research Question: How can we develop a framework for integrating information from various sources using logical rules and a hypothesis, while handling incomplete and contradictory information?

Methodology: The study uses bilattices, like Belnap's four-valued logics, to create a formal framework for information integration. This framework includes a class of programs called Fitting, and a theory for information integration.

Results: The research establishes an intuitive connection between the hypothesis testing mechanism and well-founded semantics and Kripke-Kleene semantics of Datalog programs with negation.

Implications: This research provides a comprehensive framework for integrating information from various sources, which can be applied in various scenarios, such as data warehouses and legal cases. The study also contributes to the field of logical programming and semantics.

Link to Article: https://arxiv.org/abs/0111059v1 Authors: arXiv ID: 0111059v1