Reasoning Web: Logical Foundation of Knowledge Graph Construction and Query Answering 12th International Summer School 2016, Aberdeen, UK, September 5-9, 2016, Tutorial Lectures

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 Go...

Full description

Bibliographic Details
Other Authors: Pan, Jeff Z. (Editor), Calvanese, Diego (Editor), Eiter, Thomas (Editor), Horrocks, Ian (Editor)
Format: eBook
Language:English
Published: Cham Springer International Publishing 2017, 2017
Edition:1st ed. 2017
Series:Information Systems and Applications, incl. Internet/Web, and HCI
Subjects:
Online Access:
Collection: Springer eBooks 2005- - Collection details see MPG.ReNa
LEADER 03583nmm a2200433 u 4500
001 EB001346512
003 EBX01000000000000000900702
005 00000000000000.0
007 cr|||||||||||||||||||||
008 170301 ||| eng
020 |a 9783319494937 
100 1 |a Pan, Jeff Z.  |e [editor] 
245 0 0 |a Reasoning Web: Logical Foundation of Knowledge Graph Construction and Query Answering  |h Elektronische Ressource  |b 12th International Summer School 2016, Aberdeen, UK, September 5-9, 2016, Tutorial Lectures  |c edited by Jeff Z. Pan, Diego Calvanese, Thomas Eiter, Ian Horrocks, Michael Kifer, Fangzhen Lin, Yuting Zhao 
250 |a 1st ed. 2017 
260 |a Cham  |b Springer International Publishing  |c 2017, 2017 
300 |a XIV, 259 p. 37 illus  |b online resource 
505 0 |a Understanding Author Intentions: Test Driven Knowledge Graph Construction -- Inseparability and Conservative Extensions of Description Logic Ontologies: A Survey -- Navigational and Rule-Based Languages for Graph Databases -- LOD Lab: Scalable Linked Data Processing -- Inconsistency-Tolerant Querying of Description Logic Knowledge Bases -- From Fuzzy to Annotated Semantic Web Languages -- Applying Machine Reasoning and Learning in Real World Applications 
653 |a Information Storage and Retrieval 
653 |a Artificial Intelligence 
653 |a Formal Languages and Automata Theory 
653 |a Database Management 
653 |a Data mining 
653 |a Information storage and retrieval systems 
653 |a Machine theory 
653 |a Computer Application in Administrative Data Processing 
653 |a Artificial intelligence 
653 |a Data Mining and Knowledge Discovery 
653 |a Information technology / Management 
653 |a Database management 
700 1 |a Calvanese, Diego  |e [editor] 
700 1 |a Eiter, Thomas  |e [editor] 
700 1 |a Horrocks, Ian  |e [editor] 
041 0 7 |a eng  |2 ISO 639-2 
989 |b Springer  |a Springer eBooks 2005- 
490 0 |a Information Systems and Applications, incl. Internet/Web, and HCI 
028 5 0 |a 10.1007/978-3-319-49493-7 
856 4 0 |u https://doi.org/10.1007/978-3-319-49493-7?nosfx=y  |x Verlag  |3 Volltext 
082 0 |a 005.74 
520 |a 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