Abstract
Embedding rules into Web applications, and distributed applications in general, seems to constitute a significant task in order to accommodate desired expressivity features in such environments. Various methodologies and reasoning modules have been proposed to manage rules and knowledge on the Web. The main objective of the chapter is to survey related work in this area and discuss relevant theories, methodologies and tools that can be used to develop rule-based applications for the Web. The chapter deals with both ways that have been formally defined for modeling a domain of interest: the first based on standard logics while the second one stemmed from the logic programming perspective. Furthermore, a comparative study that evaluates the reasoning engines and the various knowledge representation methodologies, focusing on rules, is presented.
Top2. The Story So Far
The knowledge representation languages proposed (see Section 3.1) for representing knowledge on the Web are based either on the Classical Logic (CL) perspective or on Logic Programming1 (LP). As a result, a debate was started between the Database community and AI researchers, respectively, in order to determine the more suitable of the two approaches in the formalization of Web knowledge. Additionally, different languages of the same perspective, providing various degrees of expressivity, have been proposed. Hence, the integration of knowledge with Web applications was more complicated. Recently, with the evolution of Semantic Web technologies, these modeling paradigms have been extensively discussed by researchers (Motik, Horrocks, Rosati, & Sattler, 2006; Patel-Schneider & Horrocks, 2006; Eiter, Ianni, Polleres, Schindlauer, & Tompits, 2006a; Donini, Lenzerini, Nardi, & Schaerf, 1998; Antoniou, et al., 2005; Boley, Kifer, Patranjan, & Polleres, 2007).
Key Terms in this Chapter
Semantic Web: It is an extension of the current Web where resources are described through formal syntax and semantics in order to be human- and machine-readable.
Logic Programming: It is a declarative paradigm of programming that is mainly based on first-order logic. Prolog is the most common logic programming language.
Ontology: A formal representation of a domain of discourse that describes concepts and relationships among them. This way, it provides a common vocabulary and allows for the inference of new knowledge.
Knowledge-Based System: A system that exploits knowledge representation and reasoning techniques in order to achieve an intelligent behavior.
Rule-Based Application: An application that takes advantage of rules formalisms (e.g., Horn-clauses) in order to represent and infer new knowledge.
Knowledge Representation: It is the process of exploiting formal methodologies and languages (e.g., rules formalisms, ontologies) in order to capture and describe knowledge.
Reasoning: The systematic process of inferring new knowledge by applying formal implication rules to a given knowledge base.