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007			cr nn 008mamaa
008			220601s2019 sz | o |||| 0|eng d
020			_a9783031794865
024	7		_a10.1007/978-3-031-79486-5 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aTK5105.88815 _b2019 EB
100	1		_aKendall, Elisa F. _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9687730
245	1	0	_aOntology Engineering _cby Elisa Kendall, Deborah McGuinness
250			_a1st edition 2019
264		1	_aCham _bSpringer International Publishing _c2019
300			_a1 recurso en línea (XVII, 102 páginas)
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aSynthesis Lectures on Data Semantics and Knowledge _x2691-2031
505	0		_aForeword: Dean Allemang -- Foreword: Richard Mark Soley, Ph.D. -- Preface -- Foundations -- Before You Begin -- Requirements and Use Cases -- Terminology -- Conceptual Modeling -- Conclusion -- Bibliography -- Author's Biographies .
520			_aOntologies have become increasingly important as the use of knowledge graphs, machine learning, natural language processing (NLP), and the amount of data generated on a daily basis has exploded. As of 2014, 90% of the data in the digital universe was generated in the two years prior, and the volume of data was projected to grow from 3.2 zettabytes to 40 zettabytes in the next six years. The very real issues that government, research, and commercial organizations are facing in order to sift through this amount of information to support decision-making alone mandate increasing automation. Yet, the data profiling, NLP, and learning algorithms that are ground-zero for data integration, manipulation, and search provide less than satisfactory results unless they utilize terms with unambiguous semantics, such as those found in ontologies and well-formed rule sets. Ontologies can provide a rich "schema" for the knowledge graphs underlying these technologies as well as the terminological and semantic basis for dramatic improvements in results. Many ontology projects fail, however, due at least in part to a lack of discipline in the development process. This book, motivated by the Ontology 101 tutorial given for many years at what was originally the Semantic Technology Conference (SemTech) and then later from a semester-long university class, is designed to provide the foundations for ontology engineering. The book can serve as a course textbook or a primer for all those interested in ontologies.
988			_aSynthesis Collection of Technology_2019
650		7	_2embne _9686599 _aOntologías (Recuperación de la información)
650		7	_2embne _9163805 _aWeb semántica
700	1		_aMcGuinness, Deborah L. _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9687731
776	0	8	_iPrinted edition: _z9783031794872
776	0	8	_iPrinted edition: _z9783031794858
776	0	8	_iPrinted edition: _z9783031794889
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-79486-5 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b03/2023 _dz _eb _zSI