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2018 | OriginalPaper | Chapter

1. Rules as a Knowledge Representation Paradigm

Author : Grzegorz J. Nalepa

Published in: Modeling with Rules Using Semantic Knowledge Engineering

Publisher: Springer International Publishing

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Abstract

Rules are a commonly used and natural way to express knowledge. They have been used for decades in AI, Computer Science, Cognitive Science and other domains. We start by discussing the AI roots of rules and elaborate on different kinds and types of rules. We then focus on a more careful treatment of rules in symbolic AI. There, they constitute an approach which allows for the representation of knowledge and basic automated reasoning. Originally, one of the most important areas for rule applications were expert systems. We will discuss them, along with a much broader perspective. What makes rule-based representation and reasoning particularly interesting is the opportunity for the formalization of rule languages. Therefore, selected logic-based formalizations are considered. We present in more detail a family of so-called attributive logics. Based on these concepts we introduce important requirements for a formalized description of rule based-systems.

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next chapter Knowledge Engineering with Rules
Footnotes
1
The idea for the project was originally proposed in [6]. See https://​www.​w3.​org/​standards/​semanticweb for currents standards.
 
2
MDA introduces three general abstraction levels in system modeling: computation independent (CIM), platform-independent (PIM) and platform-specific (PSM) [12].
 
3
Clearly this is not the only possibility. One can find a comprehensive resume of discussion on the suitability of logic for knowledge representation in [26].
 
4
The term “production” is understood technically. When a rule in such a system is fired (run) it can create a new fact which is added to the contents of the knowledge base. We can say that the rule “produced” the fact. Sometimes such rules are simply called “productions”.
 
5
There also exist rule-based languages that are meant to express the transformations of data, such as XSLT. While it uses rules, it can be argued its main purpose is not to represent rules.
 
6
In fact formalized rule languages are an important class of rule notations. They are often referred to as logic-based rule languages, as the formalization is mostly provided with means of logic.
 
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Metadata
Title
Rules as a Knowledge Representation Paradigm
Author
Grzegorz J. Nalepa
Copyright Year
2018
Book
Modeling with Rules Using Semantic Knowledge Engineering

Print ISBN: 978-3-319-66654-9

Electronic ISBN: 978-3-319-66655-6

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-66655-6_1

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