株式会社極東書店トップ ＞ 商品一覧 ＞ Reasoning Web: Logical Foundation of Knowledge Graph Construction and Query Answering: 12th International Summer School 2016, Aberdeen, UK, September 5-9, 2016, Tutorial Lectures. 1st ed. 2017
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Reasoning Web: Logical Foundation of Knowledge Graph Construction and Query Answering: 12th International Summer School 2016, Aberdeen, UK, September 5-9, 2016, Tutorial Lectures. 1st ed. 2017
・ISBN 978-3-319-49492-0 paper EUR 59.99
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| 著者・編者 | Pan, Jeff Z. / Calvanese, Diego / Eiter, Thomas / Horrocks, Ian / Kifer, Michael / Lin, Fangzhen / Zhao, Yuting (eds.), |
|---|---|
| シリーズ | (Lecture Notes in Computer Science) |
| 出版社 | (Springer International Publishing AG, SZ) |
| 出版年月 | 2017 |
| ページ数 | 259 pp. |
| 言語 | ENG |
| ニュース番号 | ＜A05-16454＞ |
解説
This volume contains some lecture notes of the 12th Reasoning Web Summer School (RW 2016), held in Aberdeen, UK, in September 2016.
In 2016, the theme of the school was "Logical Foundation of Knowledge Graph Construction and Query Answering". The notion of knowledge graph has become popular since Google started to use it to improve its search engine in 2012. Inspired by the success of Google, knowledge graphs are gaining momentum in the World Wide Web arena. Recent years have witnessed increasing industrial take-ups by other Internet giants, including Facebook's Open Graph and Microsoft's Satori.
The aim of the lecture note is to provide a logical foundation for constructing and querying knowledge graphs. Our journey starts from the introduction of Knowledge Graph as well as its history, and the construction of knowledge graphs by considering both explicit and implicit author intentions. The book will then cover various topics, including how to revise and reuseontologies (schema of knowledge graphs) in a safe way, how to combine navigational queries with basic pattern matching queries for knowledge graph, how to setup a environment to do experiments on knowledge graphs, how to deal with inconsistencies and fuzziness in ontologies and knowledge graphs, and how to combine machine learning and machine reasoning for knowledge graphs.