000 03880nam a22005775i 4500
001 978-3-031-79486-5
003 DE-He213
005 20240730164048.0
007 cr nn 008mamaa
008 220601s2019 sz | s |||| 0|eng d
020 _a9783031794865
_9978-3-031-79486-5
024 7 _a10.1007/978-3-031-79486-5
_2doi
050 4 _aQA1-939
072 7 _aPB
_2bicssc
072 7 _aMAT000000
_2bisacsh
072 7 _aPB
_2thema
082 0 4 _a510
_223
100 1 _aKendall, Elisa F.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_981883
245 1 0 _aOntology Engineering
_h[electronic resource] /
_cby Elisa F. Kendall, Deborah L. McGuinness.
250 _a1st ed. 2019.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2019.
300 _aXVII, 102 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _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.
650 0 _aMathematics.
_911584
650 0 _aInternet programming.
_935503
650 0 _aApplication software.
_981884
650 0 _aComputer networks .
_931572
650 0 _aOntology.
_95277
650 1 4 _aMathematics.
_911584
650 2 4 _aWeb Development.
_935505
650 2 4 _aComputer and Information Systems Applications.
_981885
650 2 4 _aComputer Communication Networks.
_981886
650 2 4 _aOntology.
_95277
700 1 _aMcGuinness, Deborah L.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_981887
710 2 _aSpringerLink (Online service)
_981888
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031794872
776 0 8 _iPrinted edition:
_z9783031794858
776 0 8 _iPrinted edition:
_z9783031794889
830 0 _aSynthesis Lectures on Data, Semantics, and Knowledge,
_x2691-2031
_981889
856 4 0 _uhttps://doi.org/10.1007/978-3-031-79486-5
912 _aZDB-2-SXSC
942 _cEBK
999 _c85264
_d85264