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020 _a9783031396953
_9978-3-031-39695-3
024 7 _a10.1007/978-3-031-39695-3
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082 0 4 _a658.4038
_223
100 1 _aKeet, C. Maria.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
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.
300 _aXIV, 177 p. 40 illus., 17 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
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
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