Foundations of Rule Learning by Authors: Johannes Fürnkranz, Dragan Gamberger, Nada Lavrač, ISBN: 978-3-540-75196-0 (Print) 978-3-540-75197-7 (Online).
From the Introduction:
Rule learning is not only one of the oldest but also one of the most intensively investigated, most frequently used, and best developed fields of machine learning. In more than 30 years of intensive research, many rule learning systems have been developed for propositional and relational learning, and have been successfully used in numerous applications. Rule learning is particularly useful in intelligent data analysis and knowledge discovery tasks, where the compactness of the representation of the discovered knowledge, its interpretability, and the actionability of the learned rules are of utmost importance for successful data analysis.
The aim of this book is to give a comprehensive overview of modern rule learning techniques in a unifying framework which can serve as a basis for future research and development. The book provides an introduction to rule learning in the context of other machine learning and data mining approaches, describes all the essential steps of the rule induction process, and provides an overview of practical systems and their applications. It also introduces a feature-based framework for rule learning algorithms which enables the integration of propositional and relational rule learning concepts.
The topic map parable comes near the end of the introduction where the authors note:
The book is written by authors who have been working in the field of rule learning for many years and who themselves developed several of the algorithms and approaches presented in the book. Although rule learning is assumed to be a well-established field with clearly defined concepts, it turned out that finding a unifying approach to present and integrate these concepts was a surprisingly difficult task. This is one of the reasons why the preparation of this book took more than 5 years of joint work.
A good deal of discussion went into the notation to use. The main challenge was to define a consistent notational convention to be used throughout the book because there is no generally accepted notation in the literature. The used notation is gently introduced throughout the book, and is summarized in Table I in a section on notational conventions immediately following this preface (pp. xi–xiii). We strongly believe that the proposed notation is intuitive. Its use enabled us to present different rule learning approaches in a unifying notation and terminology, hence advancing the theory and understanding of the area of rule learning.
Semantic diversity in rule learning was discovered and took five years to resolve.
Where n = all prior notations/terminologies, the solution was to create the n + 1 notation/terminology.
Understandable and certainly a major service to the rule learning community. The problem remains, how does one use the n + 1 notation/terminology to access prior (and forthcoming) literature in rule learning?
In its present form, the resolution of the prior notations and terminologies into the n + 1 terminology isn’t accessible to search, data, bibliographic engines.
Not to mention that on the next survey of rule learning, its authors will have to duplicate the work already accomplished by these authors.
Something about the inability to re-use the valuable work done by these authors, either for improvement of current information systems or to avoid duplication of effort in the future seems wrong.
Particularly since it is avoidable through the use of topic maps.
The link at the top of this post is the “new and improved site,” which has less sample content than Foundations for Rule Learning, apparently an old and not improved site.
I first saw this in a post by Gregory Piatetsky.
[…] authors of the Foundations of Rule Learning spent five years attempting to reconcile notations used in rule making. Some symbols had different […]
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