FLORA-2: A Rule-Based Knowledge Representation and Inference Infrastructure for the Semantic Web

Guizhen Yang    Michael Kifer    Chang Zhao

Abstract

FLORA-2 is a rule-based object-oriented knowledge base system designed for a variety of automated tasks on the Semantic Web, ranging from meta-data management to information integration to intelligent agents. The FLORA-2 system integrates F-logic, HiLog, and Transaction Logic into a coherent knowledge representation and inference language. The result is a flexible and natural framework that combines rule-based and object-oriented paradigms. This paper discusses the principles underlying the design of the FLORA-2 system and describes its salient features, including meta-programming, reification, logical database updates, encapsulation, and support for dynamic modules.