The principal aim of this textbook is to provide the student with a comprehensive introductory overview of ontology engineering<\/a>. A secondary aim is to provide hands-on experience in ontology development<\/a> that illustrate the theory, such as language features, automated reasoning, and top-down and bottom-up ontology development with methods and methodologies.<\/p>\n This textbook covers material such that, upon completion, the student:<\/p>\n Interwoven in the aims is skills development for a 4th year\/honours project or masters dissertation. The students will become familiar with reading scientific literature and will gain experience in report writing and presenting their work to their peers, in particular when carrying out the two suggested assignments.<\/p>\n There are review questions at the end of each chapter, whose answers can be found in the text of that chapter. Exercises are intended to obtain practical hands-on experiences and sometimes challenge the student. A selection of the exercises\u2019 answers is included in the appendix. Assignments require the student to integrate the material and, especially for the mini project, delve deeper into a specific subtopic.<\/p>\n To place \u201contologies\u201d in its right context, the first two questions one has to ask and answer are:<\/p>\n A very short and informal way of clarifying what \u201can ontology\u201d in computing is, is that it is a text file containing structured knowledge about a particular subject domain, and this file is used as a component of a so-called \u2018intelligent\u2019 information system. Fancy marketing talk may speak of some of those ontology-driven information systems as \u201clike a database, on steroids!\u201d and similar. Ontologies have been, and are being, used to solve data integration problems by providing the common, agreed-upon vocabulary which is then used in a way so that the software understands that, say, an entity Student of a relational database DB actually means the same thing as Advanced Learners in some application software OO . Tools can then be developed to link up those two applications and exchange information smoothly thanks to the shared vocabulary. Over time, people figured out other ways to use ontologies and contribute to solving entirely different problems. For instance, a question-answering system that lets the scientist chat with a library chatterbot to more easily find relevant literature (compared to string and keyword matching), automatically find a few theoretically feasible candidate rubber molecules out of very many (compared to painstaking trial-and-error work in the laboratory), and automated discovery of a new enzyme (outperforming the human experts!).<\/p>\n","protected":false},"excerpt":{"rendered":" .\n
Assessment<\/h3>\n
2.1: Prelude to Ontology Engineering<\/h3>\n
\n