| 書目名稱 | Ontology Engineering | | 編輯 | Elisa F. Kendall,Deborah L. McGuinness | | 視頻video | http://file.papertrans.cn/702/701711/701711.mp4 | | 叢書名稱 | Synthesis Lectures on Data, Semantics, and Knowledge | | 圖書封面 |  | | 描述 | Ontologies 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 1 | | 出版日期 | Book 2019 | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-031-79486-5 | | isbn_softcover | 978-3-031-79485-8 | | isbn_ebook | 978-3-031-79486-5Series ISSN 2691-2023 Series E-ISSN 2691-2031 | | issn_series | 2691-2023 | | copyright | Springer Nature Switzerland AG 2019 |
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