Researchers in Artificial Intelligence have traditionally been classified into two categories: the "neaties" and the "scruffies". According to the scruffies, the neaties concentrate on building elegant formal frameworks, whose properties are beautifully expressed by means of definitions, lemmas, and theorems, but which are of little or no use when tackling real-world problems. The scruffies are described (by the neaties) as those researchers who build superficially impressive systems that may perform extremely well on one particular case study, but whose properties and underlying theories are hidden in their implementation, if they exist at all. As a life-long, non-card-carrying scruffy, I was naturally a bit suspicious when I first started collaborating with Dieter Fensel, whose work bears all the formal hallmarks of a true neaty. Even more alarming, his primary research goal was to provide sound, formal foundations to the area of knowledge-based systems, a traditional stronghold of the scruffies - one of whom had famously declared it "an art", thus attempting to place it outside the range of the neaties (and to a large extent succeeding in doing so).
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Researchers in Artificial Intelligence have traditionally been classified into two categories: the "neaties" and the "scruffies". According to the scruffies, the neaties concentrate on building elegant formal frameworks, whose properties are beautifully expressed by means of definitions, lemmas, and theorems, but which are of little or no use when tackling real-world problems. The scruffies are described (by the neaties) as those researchers who build superficially impressive systems that may perform extremely well on one particular case study, but whose properties and underlying theories are hidden in their implementation, if they exist at all. As a life-long, non-card-carrying scruffy, I was naturally a bit suspicious when I first started collaborating with Dieter Fensel, whose work bears all the formal hallmarks of a true neaty. Even more alarming, his primary research goal was to provide sound, formal foundations to the area of knowledge-based systems, a traditional stronghold of the scruffies - one of whom had famously declared it "an art", thus attempting to place it outside the range of the neaties (and to a large extent succeeding in doing so).
This book provides a theory, a formal language, and a practical methodology for the specification, use, and reuse of problem-solving methods. The framework developed by the author characterizes knowledge-based systems as a particular type of software architecture where the applications are developed by integrating generic task specifications, problem solving methods, and domain models: this approach turns knowledge engineering into a software engineering discipline. All in all, this work, as an applicable theory of knowledge engineering, consolidates research work done during several decades. The present popularity of Internet-based services will provide unprecedented opportunities for deploying and sharing knowledge-based services and anybody wanting to participate in this area can learn from this book what knowledge engineering is about.
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