Many of the potential obstacles to adoption of the Semantic Web are similar if not identical to the prbblems faced by rule-based systems in the late 1980s. Therefore, the business rules methodology may be a good guide for development of the Semantic Web.
First, some basic principles.
This entire section contains very few mentions of computers or discussions about about computer implementations or, even worse, about the 'best' computer languages. The following sections are far more concerned with the subject of how people reason about the world and things in the world than it is about computers. I think that 'reasoning' is the proper level of inquiry for 'rule based systems', rather than focusing too much on the technical considerations which too often come to dominate and obscure the underlying issue of how people do what they do when solving common problems.
But, the types of situations which will be encountered in the building of a semantic web may not be as well-defined as those encountered in business processes. The shadow of incomplete, inconsistent and outright unreliable information looms on every corner of Web. How can a set of well-defined rules deal with ill-defined information ?
Can new definitions for rules, rule base and rule engine be found which will be sufficiently precise to meet the level of exactitude demanded by computer applications and still be fuzzy enough to capture all the nuances of inexact reasoning ? I think the answer is, maybe.
In fact, by lumping inexact reasoning in our definition, we are returning to an earlier, more inclusive definition of rule based systems as describing how people tend to reason in different situations, probably more similar to 'cognitive science' as currently defined. For example, an expanded definition would include the tricky subjects of truth maintenance and belief revision, subject well outside the realm of business rules or classical expert systems.
Oct 21 2008: the Wikipedia entry for "Knowledge Technologies" deleted recently, we seem to be going backwards ...
The definition of 'rule' can be extended beyond sense of 'exact reasoning' implicit in the business rules definition of the word. A 'rule' in the larger sense could be more than an exact expression of business logic, it could also be a expression of inexact reasoning, such as is a judgment about taking an umbrella along or leaving it behind. Potentially, the decision could include inexact criteria, such as the decision whether to bring along an umbrella for a morning walk on a misty fog-shrouded beach. Of course, the correct answer is "no", for me anyway.
A broader definition of 'rule' can extend well beyond the narrow sense of deductive systems encountered in rule-based "expert" systems and their kin. This broader definition of rules and rule-based technology includes inexact reasoning based on associations inferred between the subjects of a rule. Inference by association uses the inductive and abductive modes of inference and a different set of inference engines, such as associative networks, fuzzy logic, 'case-based reasoning' or any other inferential tools that work by association rather than deductive logic.
In this context, the definition of 'rule based systems' is similar to 'knowledge based systems', if more focused on logic inference and less abstract in its application than KBS. In fact, the Wikipedia may have a better name for it than either 'knowledge based systems' or a 'rule based systems', that is knowledge technology ( recently deleted, alas ).
The term knowledge technologies refers to a fuzzy set of tools including languages and software enabling better representation, organization and exchange of information and knowledge ...
Among knowledge technologies are ontologies, topic maps, blogs, groupware, document management, expertise locators, latent semantic analysis, semantic networks, social networking engines, and wikis.
This sounds very close to the 'broader' definition outlined above.