000 | 03096nam a22005535i 4500 | ||
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001 | 978-3-031-30387-6 | ||
003 | DE-He213 | ||
005 | 20240730165337.0 | ||
007 | cr nn 008mamaa | ||
008 | 230603s2023 sz | s |||| 0|eng d | ||
020 |
_a9783031303876 _9978-3-031-30387-6 |
||
024 | 7 |
_a10.1007/978-3-031-30387-6 _2doi |
|
050 | 4 | _aQ334-342 | |
050 | 4 | _aTA347.A78 | |
072 | 7 |
_aUYQ _2bicssc |
|
072 | 7 |
_aCOM004000 _2bisacsh |
|
072 | 7 |
_aUYQ _2thema |
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082 | 0 | 4 |
_a006.3 _223 |
100 | 1 |
_aPaulheim, Heiko. _eauthor. _0(orcid) _10000-0003-4386-8195 _4aut _4http://id.loc.gov/vocabulary/relators/aut _988514 |
|
245 | 1 | 0 |
_aEmbedding Knowledge Graphs with RDF2vec _h[electronic resource] / _cby Heiko Paulheim, Petar Ristoski, Jan Portisch. |
250 | _a1st ed. 2023. | ||
264 | 1 |
_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2023. |
|
300 |
_aIX, 158 p. 43 illus., 27 illus. in color. _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 |
||
490 | 1 |
_aSynthesis Lectures on Data, Semantics, and Knowledge, _x2691-2031 |
|
505 | 0 | _aIntroduction -- From Word Embeddings to Knowledge Graph Embeddings -- RDF2vec Variants and Representations -- Tweaking RDF2vec -- RDF2vec at Scale -- Example Applications beyond Node Classification -- Link Prediction in Knowledge Graphs (and its Relation to RDF2vec) -- Future Directions for RDF2vec. | |
520 | _aThis book explains the ideas behind one of the most well-known methods for knowledge graph embedding of transformations to compute vector representations from a graph, known as RDF2vec. The authors describe its usage in practice, from reusing pre-trained knowledge graph embeddings to training tailored vectors for a knowledge graph at hand. They also demonstrate different extensions of RDF2vec and how they affect not only the downstream performance, but also the expressivity of the resulting vector representation, and analyze the resulting vector spaces and the semantic properties they encode. | ||
650 | 0 |
_aArtificial intelligence. _93407 |
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650 | 0 |
_aData mining. _93907 |
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650 | 0 |
_aExpert systems (Computer science). _93392 |
|
650 | 1 | 4 |
_aArtificial Intelligence. _93407 |
650 | 2 | 4 |
_aData Mining and Knowledge Discovery. _988517 |
650 | 2 | 4 |
_aKnowledge Based Systems. _979172 |
700 | 1 |
_aRistoski, Petar. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _988519 |
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700 | 1 |
_aPortisch, Jan. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _988521 |
|
710 | 2 |
_aSpringerLink (Online service) _988524 |
|
773 | 0 | _tSpringer Nature eBook | |
776 | 0 | 8 |
_iPrinted edition: _z9783031303869 |
776 | 0 | 8 |
_iPrinted edition: _z9783031303883 |
776 | 0 | 8 |
_iPrinted edition: _z9783031303890 |
830 | 0 |
_aSynthesis Lectures on Data, Semantics, and Knowledge, _x2691-2031 _988526 |
|
856 | 4 | 0 | _uhttps://doi.org/10.1007/978-3-031-30387-6 |
912 | _aZDB-2-SXSC | ||
942 | _cEBK | ||
999 |
_c86261 _d86261 |