000 | 03560nam a22005895i 4500 | ||
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001 | 978-3-030-12375-8 | ||
003 | DE-He213 | ||
005 | 20240423125251.0 | ||
007 | cr nn 008mamaa | ||
008 | 190304s2019 sz | s |||| 0|eng d | ||
020 |
_a9783030123758 _9978-3-030-12375-8 |
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024 | 7 |
_a10.1007/978-3-030-12375-8 _2doi |
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050 | 4 | _aQA76.9.D343 | |
072 | 7 |
_aUNF _2bicssc |
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_aUYQE _2thema |
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082 | 0 | 4 |
_a006.312 _223 |
100 | 1 |
_aKejriwal, Mayank. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
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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. |
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300 |
_aXIV, 107 p. 19 illus. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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338 |
_aonline resource _bcr _2rdacarrier |
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347 |
_atext file _bPDF _2rda |
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490 | 1 |
_aSpringerBriefs in Computer Science, _x2191-5776 |
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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. |
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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 |