LEADER 04007nam  22006015  450 
001 9910163028403321
005 20200705143814.0
010   $a3-319-45654-7
024 7 $a10.1007/978-3-319-45654-6
035   $a(CKB)3710000001045309
035   $a(DE-He213)978-3-319-45654-6
035   $a(MiAaPQ)EBC4799365
035   $a(PPN)198872496
035   $a(EXLCZ)993710000001045309
100   $a20170202d2017      u|             0
101 0 $aeng
135   $aurnn|008mamaa
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aExploiting Linked Data and Knowledge Graphs in Large Organisations$b[electronic resource] /$fedited by Jeff Z. Pan, Guido Vetere, Jose Manuel Gomez-Perez, Honghan Wu
205   $a1st ed. 2017.
210  1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2017.
215   $a1 online resource (XVIII, 266 p. 59 illus., 44 illus. in color.) 
311   $a3-319-45652-0 
320   $aIncludes bibliographical references and index.
327   $aPart I Knowledge Graph Foundations & Architecture -- Part II Constructing, Understanding and Consuming Knowledge Graphs -- Part III Industrial Applications and Successful Stories.
330   $aThis book addresses the topic of exploiting enterprise-linked data with a particular focus on knowledge construction and accessibility within enterprises. It identifies the gaps between the requirements of enterprise knowledge consumption and ?standard? data consuming technologies by analysing real-world use cases, and proposes the enterprise knowledge graph to fill such gaps. It provides concrete guidelines for effectively deploying linked-data graphs within and across business organizations. It is divided into three parts, focusing on the key technologies for constructing, understanding and employing knowledge graphs. Part 1 introduces basic background information and technologies, and presents a simple architecture to elucidate the main phases and tasks required during the lifecycle of knowledge graphs. Part 2 focuses on technical aspects; it starts with state-of-the art knowledge-graph construction approaches, and then discusses exploration and exploitation techniques as well as advanced question-answering topics concerning knowledge graphs. Lastly, Part 3 demonstrates examples of successful knowledge graph applications in the media industry, healthcare and cultural heritage, and offers conclusions and future visions.
606   $aArtificial intelligence
606   $aData mining
606   $aApplication software
606   $aManagement information systems
606   $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000
606   $aData Mining and Knowledge Discovery$3https://scigraph.springernature.com/ontologies/product-market-codes/I18030
606   $aInformation Systems Applications (incl. Internet)$3https://scigraph.springernature.com/ontologies/product-market-codes/I18040
606   $aBusiness Information Systems$3https://scigraph.springernature.com/ontologies/product-market-codes/522030
615  0$aArtificial intelligence.
615  0$aData mining.
615  0$aApplication software.
615  0$aManagement information systems.
615 14$aArtificial Intelligence.
615 24$aData Mining and Knowledge Discovery.
615 24$aInformation Systems Applications (incl. Internet).
615 24$aBusiness Information Systems.
676   $a006.3
702   $aPan$b Jeff Z$4edt$4http://id.loc.gov/vocabulary/relators/edt
702   $aVetere$b Guido$4edt$4http://id.loc.gov/vocabulary/relators/edt
702   $aGomez-Perez$b Jose Manuel$4edt$4http://id.loc.gov/vocabulary/relators/edt
702   $aWu$b Honghan$4edt$4http://id.loc.gov/vocabulary/relators/edt
906   $aBOOK
912   $a9910163028403321
996   $aExploiting Linked Data and Knowledge Graphs in Large Organisations$92540312
997   $aUNINA