Amalgamating knowledge bases, III: Algorithms, data structures and query processing

Abstract

Integrating knowledge from multiple sources is an important aspect of automated reasoning systems. In the first part of this series of papers, we presented a uniform declarative framework, based on annotated logics, for amalgamating multiple knowledge bases when these knowledge bases (possibly) contain inconsistencies, uncertainties, and nonmonotonic modes of negation. We showed that annotated logics may be used, with some modifications, to mediate between different knowledge bases. The multiple knowledge bases are amalgamated by embedding the individual knowledge bases into a lattice. In this paper, we briefly describe an SLD-resolution-based proof procedure that is sound and complete w.r.t. our declarative semantics. We will then develop an OLDT-resolution-based query processing procedure, MULTI_OLDT, that satisfies two important properties: (1) efficient reuse of previous computations is achieved by maintaining a table—we describe the structure of this table, and show that table operations can be efficiently executed, and (2) approximate, interruptable query answering is achieved, i.e., it is possible to obtain an “intermediate, approximate” answer from the QPP by interrupting it at any point in time during its execution. The design of the MULTI_OLDT procedure will include the development of run-time algorithms to incrementally and efficiently update the table.