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020 _a9783030123758
_9978-3-030-12375-8
024 7 _a10.1007/978-3-030-12375-8
_2doi
050 4 _aQA76.9.D343
072 7 _aUNF
_2bicssc
072 7 _aUYQE
_2bicssc
072 7 _aCOM021030
_2bisacsh
072 7 _aUNF
_2thema
072 7 _aUYQE
_2thema
082 0 4 _a006.312
_223
100 1 _aKejriwal, Mayank.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aDomain-Specific Knowledge Graph Construction
_h[electronic resource] /
_cby Mayank Kejriwal.
250 _a1st ed. 2019.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2019.
300 _aXIV, 107 p. 19 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSpringerBriefs in Computer Science,
_x2191-5776
505 0 _a1. What is a knowledge graph? -- 2. Information Extraction -- 3. Entity Resolution -- 4. Advanced Topic: Knowledge Graph Completion -- 5. Ecosystems .
520 _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.
650 0 _aData mining.
650 0 _aInformation storage and retrieval systems.
650 0 _aApplication software.
650 0 _aComputer science
_xMathematics.
650 0 _aMathematical statistics.
650 1 4 _aData Mining and Knowledge Discovery.
650 2 4 _aInformation Storage and Retrieval.
650 2 4 _aComputer and Information Systems Applications.
650 2 4 _aProbability and Statistics in Computer Science.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783030123741
776 0 8 _iPrinted edition:
_z9783030123765
830 0 _aSpringerBriefs in Computer Science,
_x2191-5776
856 4 0 _uhttps://doi.org/10.1007/978-3-030-12375-8
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
942 _cSPRINGER
999 _c176086
_d176086