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024 7 _a10.1007/978-3-319-49493-7
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245 1 0 _aReasoning Web: Logical Foundation of Knowledge Graph Construction and Query Answering
_h[electronic resource] :
_b12th International Summer School 2016, Aberdeen, UK, September 5-9, 2016, Tutorial Lectures /
_cedited by Jeff Z. Pan, Diego Calvanese, Thomas Eiter, Ian Horrocks, Michael Kifer, Fangzhen Lin, Yuting Zhao.
250 _a1st ed. 2017.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2017.
300 _aXIV, 259 p. 37 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
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490 1 _aInformation Systems and Applications, incl. Internet/Web, and HCI,
_x2946-1642 ;
_v9885
505 0 _aUnderstanding 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.
520 _aThis 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.
650 0 _aDatabase management.
_93157
650 0 _aArtificial intelligence.
_93407
650 0 _aMachine theory.
_982798
650 0 _aInformation storage and retrieval systems.
_922213
650 0 _aInformation technology
_xManagement.
_95368
650 0 _aData mining.
_93907
650 1 4 _aDatabase Management.
_93157
650 2 4 _aArtificial Intelligence.
_93407
650 2 4 _aFormal Languages and Automata Theory.
_982802
650 2 4 _aInformation Storage and Retrieval.
_923927
650 2 4 _aComputer Application in Administrative Data Processing.
_931588
650 2 4 _aData Mining and Knowledge Discovery.
_982804
700 1 _aPan, Jeff Z.
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700 1 _aCalvanese, Diego.
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700 1 _aEiter, Thomas.
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700 1 _aHorrocks, Ian.
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700 1 _aKifer, Michael.
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700 1 _aLin, Fangzhen.
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700 1 _aZhao, Yuting.
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_982815
710 2 _aSpringerLink (Online service)
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773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
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776 0 8 _iPrinted edition:
_z9783319494944
830 0 _aInformation Systems and Applications, incl. Internet/Web, and HCI,
_x2946-1642 ;
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_982819
856 4 0 _uhttps://doi.org/10.1007/978-3-319-49493-7
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