LEADER 03610nam  22006135  450 
001 9910337574903321
005 20251113193539.0
010   $a3-030-12375-8
024 7 $a10.1007/978-3-030-12375-8
035   $a(CKB)4100000007758395
035   $a(MiAaPQ)EBC5724748
035   $a(DE-He213)978-3-030-12375-8
035   $a(PPN)235232653
035   $a(EXLCZ)994100000007758395
100   $a20190304d2019      u|         0
101 0 $aeng
135   $aurcnu||||||||
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aDomain-Specific Knowledge Graph Construction /$fby Mayank Kejriwal
205   $a1st ed. 2019.
210  1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2019.
215   $a1 online resource (115 pages)
225 1 $aSpringerBriefs in Computer Science,$x2191-5776
311 08$a3-030-12374-X 
327   $a1. What is a knowledge graph? -- 2. Information Extraction -- 3. Entity Resolution -- 4. Advanced Topic: Knowledge Graph Completion -- 5. Ecosystems .
330   $aThe vast amounts of ontologically unstructured information on the Web, including HTML, XML and JSON documents, natural language documents, tweets, blogs, markups, and even structured documents like CSV tables, all contain useful knowledge that can present a tremendous advantage to the Artificial Intelligence community if extracted robustly, efficiently and semi-automatically as knowledge graphs. Domain-specific Knowledge Graph Construction (KGC) is an active research area that has recently witnessed impressive advances due to machine learning techniques like deep neural networks and word embeddings. This book will synthesize Knowledge Graph Construction over Web Data in an engaging and accessible manner. The book describes a timely topic for both early -and mid-career researchers. Every year, more papers continue to be published on knowledge graph construction, especially for difficult Web domains. This book serves as a useful reference, as well as anaccessible but rigorous overview of this body of work. The book presents interdisciplinary connections when possible to engage researchers looking for new ideas or synergies. The book also appeals to practitioners in industry and data scientists since it has chapters on both data collection, as well as a chapter on querying and off-the-shelf implementations.
410  0$aSpringerBriefs in Computer Science,$x2191-5776
606   $aData mining
606   $aInformation storage and retrieval systems
606   $aApplication software
606   $aComputer science$xMathematics
606   $aMathematical statistics
606   $aData Mining and Knowledge Discovery
606   $aInformation Storage and Retrieval
606   $aComputer and Information Systems Applications
606   $aProbability and Statistics in Computer Science
615  0$aData mining.
615  0$aInformation storage and retrieval systems.
615  0$aApplication software.
615  0$aComputer science$xMathematics.
615  0$aMathematical statistics.
615 14$aData Mining and Knowledge Discovery.
615 24$aInformation Storage and Retrieval.
615 24$aComputer and Information Systems Applications.
615 24$aProbability and Statistics in Computer Science.
676   $a511.5
676   $a006.33
700   $aKejriwal$b Mayank$4aut$4http://id.loc.gov/vocabulary/relators/aut$01063580
906   $aBOOK
912   $a9910337574903321
996   $aDomain-Specific Knowledge Graph Construction$92533034
997   $aUNINA