000 | 04149nam a22005535i 4500 | ||
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001 | 978-3-031-39695-3 | ||
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_a9783031396953 _9978-3-031-39695-3 |
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024 | 7 |
_a10.1007/978-3-031-39695-3 _2doi |
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_a658.4038 _223 |
100 | 1 |
_aKeet, C. Maria. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
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245 | 1 | 4 |
_aThe What and How of Modelling Information and Knowledge _h[electronic resource] : _bFrom Mind Maps to Ontologies / _cby C. Maria Keet. |
250 | _a1st ed. 2023. | ||
264 | 1 |
_aCham : _bSpringer Nature Switzerland : _bImprint: Springer, _c2023. |
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300 |
_aXIV, 177 p. 40 illus., 17 illus. in color. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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338 |
_aonline resource _bcr _2rdacarrier |
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347 |
_atext file _bPDF _2rda |
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505 | 0 | _a1. Introduction: Why Modelling? -- 2. Mind Maps -- 3. Models and Diagrams in Biology -- 4. Conceptual Data Models -- 5. Ontologies and Similar Artefacts -- 6. Ontology—With a Capital O -- 7. Fit For Purpose -- 8. Go Forth and Model. | |
520 | _aThe main aim of this book is to introduce a group of models and modelling of information and knowledge comprehensibly. Such models and the processes for how to create them help to improve the skills to analyse and structure thoughts and ideas, to become more precise, to gain a deeper understanding of the matter being modelled, and to assist with specific tasks where modelling helps, such as reading comprehension and summarisation of text. The book draws ideas and transferrable approaches from the plethora of types of models and the methods, techniques, tools, procedures, and methodologies to create them in computer science. This book covers five principal declarative modelling approaches to model information and knowledge for different, yet related, purposes. It starts with entry-level mind mapping, to proceed to biological models and diagrams, onward to conceptual data models in software development, and from there to ontologies in artificial intelligence and all the way to ontologyin philosophy. Each successive chapter about a type of model solves limitations of the preceding one and turns up the analytical skills a notch. These what-and-how for each type of model is followed by an integrative chapter that ties them together, comparing their strengths and key characteristics, ethics in modelling, and how to design a modelling language. In so doing, we’ll address key questions such as: what type of models are there? How do you build one? What can you do with a model? Which type of model is best for what purpose? Why do all that modelling? The intended audience for this book is professionals, students, and academics in disciplines where systematic information modelling and knowledge representation is much less common than in computing, such as in commerce, biology, law, and humanities. And if a computer science student or a software developer needs a quick refresher on conceptual data models or a short solid overview of ontologies, then this book will servethem well. | ||
650 | 0 | _aKnowledge management. | |
650 | 0 | _aInformation modeling. | |
650 | 0 | _aAnalysis (Philosophy). | |
650 | 0 | _aApplication software. | |
650 | 0 | _aSoftware engineering. | |
650 | 1 | 4 | _aKnowledge Management. |
650 | 2 | 4 | _aInformation Model. |
650 | 2 | 4 | _aConceptual Analysis. |
650 | 2 | 4 | _aComputer and Information Systems Applications. |
650 | 2 | 4 | _aSoftware Engineering. |
710 | 2 | _aSpringerLink (Online service) | |
773 | 0 | _tSpringer Nature eBook | |
776 | 0 | 8 |
_iPrinted edition: _z9783031396946 |
776 | 0 | 8 |
_iPrinted edition: _z9783031396960 |
856 | 4 | 0 | _uhttps://doi.org/10.1007/978-3-031-39695-3 |
912 | _aZDB-2-SCS | ||
912 | _aZDB-2-SXCS | ||
942 | _cSPRINGER | ||
999 |
_c186042 _d186042 |