Non-parametric Learning of Embeddings for Relational Data Using Gaifman Locality Theorem.

Devendra Singh Dhami; Siwen Yan; Gautam Kunapuli; Sriraam Natarajan

doi:10.1007/978-3-030-97454-1_7

Non-parametric Learning of Embeddings for Relational Data Using Gaifman Locality Theorem.

Devendra Singh Dhami, Siwen Yan, Gautam Kunapuli, Sriraam Natarajan

Uncertainty in Artificial Intelligence

Research output: Contribution to conference › Paper › Academic

1 Citation (Scopus)

Original language	Undefined
Pages	95-110
DOIs	https://doi.org/10.1007/978-3-030-97454-1_7
Publication status	Published - 2021

Bibliographical note

DBLP's bibliographic metadata records provided through http://dblp.org/search/publ/api are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.

Access to Document

10.1007/978-3-030-97454-1_7

https://dblp.org/rec/conf/ilp/DhamiYKN21

Cite this

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title = "Non-parametric Learning of Embeddings for Relational Data Using Gaifman Locality Theorem.",

author = "Dhami, {Devendra Singh} and Siwen Yan and Gautam Kunapuli and Sriraam Natarajan",

note = "DBLP's bibliographic metadata records provided through http://dblp.org/search/publ/api are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.",

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AU - Yan, Siwen

AU - Kunapuli, Gautam

AU - Natarajan, Sriraam

N1 - DBLP's bibliographic metadata records provided through http://dblp.org/search/publ/api are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.

PY - 2021

Y1 - 2021

U2 - 10.1007/978-3-030-97454-1_7

DO - 10.1007/978-3-030-97454-1_7

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EP - 110

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