| 书目名称 | Knowledge Graphs | | 编辑 | Aidan Hogan,Claudio Gutierrez,Antoine Zimmermann | | 视频video | http://file.papertrans.cn/544/543936/543936.mp4 | | 丛书名称 | Synthesis Lectures on Data, Semantics, and Knowledge | | 图书封面 |  | | 描述 | This book provides a comprehensive and accessible introduction to knowledge graphs, which have recently garnered notable attention from both industry and academia. Knowledge graphs are founded on the principle of applying a graph-based abstraction to data, and are now broadly deployed in scenarios that require integrating and extracting value from multiple, diverse sources of data at large scale. The book defines knowledge graphs and provides a high-level overview of how they are used. It presents and contrasts popular graph models that are commonly used to represent data as graphs, and the languages by which they can be queried before describing how the resulting data graph can be enhanced with notions of schema, identity, and context. The book discusses how ontologies and rules can be used to encode knowledge as well as how inductive techniques—based on statistics, graph analytics, machine learning, etc.—can be used to encode and extract knowledge. It covers techniques for the creation, enrichment, assessment, and refinement of knowledge graphs and surveys recent open and enterprise knowledge graphs and the industries or applications within which they have been most widely adopte | | 出版日期 | Book 2022 | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-031-01918-0 | | isbn_softcover | 978-3-031-00790-3 | | isbn_ebook | 978-3-031-01918-0Series ISSN 2691-2023 Series E-ISSN 2691-2031 | | issn_series | 2691-2023 | | copyright | Springer Nature Switzerland AG 2022 |
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