Books : Ontological Engineering: with examples from the areas of Knowledge Management, e-Commerce and the Semantic Web. First Edition (Advanced Information and Knowledge Processing)
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Type of bind: Hardcover
Dewey Decimal Number: 006.33
EAN num: 9781852335519
ISBN number: 1852335513
Label: Springer
Manufacturer: Springer
Quantity: 1
Page Count: 415
Printing Date: July 22, 2004
Publishing house: Springer
Sale Popularity Level: 399259
Studio: Springer
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Ontologies provide a common vocabulary of an area and define - with different levels of formality - the meaning of the terms and the relationships between them. Ontologies may be reused and shared across applications and groups Concepts in the ontology are usually organized in taxonomies and relations between concepts, properties of concepts, and axioms are typically used for representing the knowledge contained in ontologies. With the growth of information available, e.g. on the WWW, they are popularly applied in knowledge management, semantic web, natural language generation, enterprise modelling, knowledge-based systems, ontology-based brokers, e-commerce platforms and interoperability between systems. This book looks at questions such as: * What is an ontology? * What are the uses of ontologies? * What types of ontologies exist? What are the most well-known ones? * How do I select the best ontology for my application? * What are the principles for building an ontology? * What methodologies should I use to build my own ontology? Which techniques are appropriate for each step? * How do software tools support the process of building and using ontologies? * What language can I use to implement ontologies? * How can I integrate ontologies in a given language? The book presents the theoretical foundations of ontological engineering and covers the practical aspects of selecting and applying methodologies, tools and languages for building ontologies. The applications of ontologies are also illustrated with case studies taken from the areas of knowledge management, e-commerce and the semantic web.
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Rated by buyers 
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The subject matter is much too complex, does not follow a logical order, is a slow and arduous read, and is not practical.
This book was the subject of a book club where I and a small group of software engineers wanted to learn more about ontologies. Most of the members of the group had some experience with ontology languages. In each one-hour lunch session, we were not able to discuss more than 10 pages at a time due to the complexity of the writing and the subject matter. We finally gave up and none of us has finished the book. Although we read over half of the book before giving up, we gained no practical knowledge from it whatsoever.
Rated by buyers 
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The book shows progress in how ontologies are defined from various data sets. The subject is a natural field of artificial intelligence, in attempting to automated this filling of an ontology. Various example ontologies are presented, along with the markup languages like RDF and OWL in which these are expressed. The progress is visible, inasmuch as just a few years ago, these languages were devised. Now we see non-trivial ontology constructions using them. Good.
A large portion of the book describes the acute problem of somehow extracting meaning in a programmatic manner from data. Because the manual making of an ontology simply does not seem to scale, given the realities of gigabyte databases. We see that there is a natural decomposition of the problem into a linguistic step and a conceptual step. The former is tied to a particular human language. The latter is the nut of the problem. Current methods look promising, but are certainly not the last word.
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The book is well organized in introducing the subject in a coherent manner and weaving in all important criteria of ontology together. I especially like to read the comparison of different languagees in light of knowlege represenation and knowlege reasoing. The book is great in terms of getting a broad view (survey) and is also great as a reference. In many pages, there is so much information packed in each sentences. Great book.
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The word `ontology' is usually associated with philosophical speculation on the reality of things, and if one checks the literature on philosophy one will find a diverse number of opinions on this reality. Engineers and scientists typically view philosophical musings on any topic as being impractical, and indulging oneself in these musings will cause one to lose sight of the topic or problem at hand. Rather than simplify the problem and make it understandable, philosophy tends in most cases to complicate it by endless debate on definitions and the use of sophisticated rhetoric that seems to have no bearing on the problem at hand. The conceptual spaces generated by these debates can become gigantic and therefore unwieldy, thus making the problem appear more complex than it actually is.
In the information age however, ontology has become a word that has taken on enormous practical significance. Business and scientific research are both areas that have increasingly relied on information technology not only to organize information but also to analyze data and make accurate predictions. In addition, financial constraints have forced many businesses to automate most of their internal processes, and this automation has brought about its own unique challenges. This push to automation usually involves being able to differentiate one thing from another, or one collection of data from another, or one concept from another. Thus one needs to think about questions of ontology, and this (very practical) need has brought about the rise of the field of `ontological engineering', which is the topic of this book.
The authors have given a good general overview of the different approaches to the creation of ontologies. There are many of them, some of which seem "natural", while others seem more esoteric. The reader though will obtain an objective discusion of the ontologies that the authors chose to include in the book. Discussions of the ones that are not included can readily be found on the Internet.
Given the plethora of ontologies that have been invented, it would be of interest to the ontological engineer to find common ground between them. The re-use of a particular ontology may be stymied by the different ontological commitments it is adhering to or it's actual content. In order to use it, it must therefore be "re-engineered". The authors discuss this prospect in the book, and define `ontological re-engineering' as the process where a conceptual model of an implemented ontology is transformed into one that is more suitable. The code in which the ontology is written is very first reverse engineered, and then the conceptual model is reorganized into the new one. The new conceptual model is then implemented.
Also discussed in the book, and of enormous practical interest, is the automation of the ontology building process. Called `ontology learning' by the authors, they discuss a few of the ways in which this could take place. One of these methods concerns ontology learning using a `corpus of texts', and involves being able to distinguish between the `linguistic' and `conceptual' levels. Knowledge at the linguistic level is described in linguistic terms, while at the conceptual level in terms of concepts and the relations between them. Ontology learning is thus dependent on how the linguistic structures are exemplified in the conceptual level. Relations at the conceptual level for example could be extracted from sequences of words in the text that conform to a certain pattern. Another method comes from data mining and involves the use of association rules to find relations between concepts. The authors discuss two well-known methods for ontology learning from texts. Both of these methods are interesting in that they can apparently learn in contexts or environments that are not domain-specific. Being able to learn over different domains is very important from the standpoint of the artificial intelligence community and these methods are a step in that direction. The processes of `alignment', `merging', and `cooperative construction' of ontologies that are discussed in the book are also of great interest in artificial intelligence, since they too will be of assistance in the endeavor to design a machine that can reason over multiple domains.
The ontologies that are actually built are of course not unique. This results in a kind of semantic or cognitive relativism between the environments that might be built on different ontologies, even in the same domain. Merging and alignment both address this relativism, along with other techniques that are discussed in the book. The selection of the actual language that is used to create an ontology is also somewhat arbitrary. The authors devote a fair amount of space in the book to the different languages that have been used to build ontologies. Through an elementary example, they discuss eleven different languages, namely KIF, ... Read More
Rated by buyers -
The subject of this book is incredibly relevant to today's world of information management. The chapters are presented in a logical and informative way, though some of the book only skims the surface or barely touches on significant developments, tools, and problems. Overall, I found the text too theoretical, with insufficient ties to messy real-world issues.
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