Distributed semantic analytics using the SANSA stack

Jens Lehmann; Gezim Sejdiu; Lorenz Bühmann; Patrick Westphal; Claus Stadler; Ivan Ermilov; Nilesh Chakraborty; Simon Bin; Muhammad Saleem; Axel Cyrille Ngonga Ngomo; Hajira Jabeen

doi:10.1007/978-3-319-68204-4_15

Distributed semantic analytics using the SANSA stack

Jens Lehmann^*
, Gezim Sejdiu
, Lorenz Bühmann
, Patrick Westphal
, Claus Stadler
, Ivan Ermilov
, Nilesh Chakraborty
, Simon Bin
, Muhammad Saleem
, Axel Cyrille Ngonga Ngomo
, Hajira Jabeen

^*Corresponding author for this work

Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review

65 Citations (Scopus)

Abstract

A major research challenge is to perform scalable analysis of large-scale knowledge graphs to facilitate applications like link prediction, knowledge base completion and reasoning. Analytics methods which exploit expressive structures usually do not scale well to very large knowledge bases, and most analytics approaches which do scale horizontally (i.e., can be executed in a distributed environment) work on simple feature-vector-based input. This software framework paper describes the ongoing Semantic Analytics Stack (SANSA) project, which supports expressive and scalable semantic analytics by providing functionality for distributed computing on RDF data.

Original language	English
Title of host publication	The Semantic Web – ISWC 2017 - 16th International Semantic Web Conference, Proceedings
Editors	Miriam Fernandez, Claudia d’Amato, Valentina Tamma, Philippe Cudre-Mauroux, Freddy Lecue, Christoph Lange, Juan Sequeda, Jeff Heflin
Number of pages	9
Publisher	Springer-Verlag Austria
Publication date	2017
Pages	147-155
ISBN (Print)	978-3-319-68203-7
ISBN (Electronic)	978-3-319-68204-4
DOIs	https://doi.org/10.1007/978-3-319-68204-4_15
Publication status	Published - 2017
Externally published	Yes
Event	16th International Semantic Web Conference, ISWC 2017 - Vienna, Austria Duration: 21.10.2017 → 25.10.2017 Conference number: 16

Bibliographical note

Publisher Copyright:
© Springer International Publishing AG 2017.

Research areas and keywords

Informatics
Computer science
Analytics
Exploit
Scalability
Semantic analytics
RDF
Data science
Knowledge base
Semantic Web
Stack (abstract data type)
World Wide Web
Programming language
Semantic computing
Database

ASJC Scopus Subject Areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-319-68204-4_15

Cite this

Lehmann, J., Sejdiu, G., Bühmann, L., Westphal, P., Stadler, C., Ermilov, I., Chakraborty, N., Bin, S., Saleem, M., Ngonga Ngomo, A. C., & Jabeen, H. (2017). Distributed semantic analytics using the SANSA stack. In M. Fernandez, C. d’Amato, V. Tamma, P. Cudre-Mauroux, F. Lecue, C. Lange, J. Sequeda, & J. Heflin (Eds.), The Semantic Web – ISWC 2017 - 16th International Semantic Web Conference, Proceedings (pp. 147-155). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10588 LNCS). Springer-Verlag Austria. https://doi.org/10.1007/978-3-319-68204-4_15

Lehmann, Jens ; Sejdiu, Gezim ; Bühmann, Lorenz et al. / Distributed semantic analytics using the SANSA stack. The Semantic Web – ISWC 2017 - 16th International Semantic Web Conference, Proceedings. editor / Miriam Fernandez ; Claudia d’Amato ; Valentina Tamma ; Philippe Cudre-Mauroux ; Freddy Lecue ; Christoph Lange ; Juan Sequeda ; Jeff Heflin. Springer-Verlag Austria, 2017. pp. 147-155 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "A major research challenge is to perform scalable analysis of large-scale knowledge graphs to facilitate applications like link prediction, knowledge base completion and reasoning. Analytics methods which exploit expressive structures usually do not scale well to very large knowledge bases, and most analytics approaches which do scale horizontally (i.e., can be executed in a distributed environment) work on simple feature-vector-based input. This software framework paper describes the ongoing Semantic Analytics Stack (SANSA) project, which supports expressive and scalable semantic analytics by providing functionality for distributed computing on RDF data.",

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year = "2017",

doi = "10.1007/978-3-319-68204-4\_15",

language = "English",

isbn = "978-3-319-68203-7",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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Lehmann, J, Sejdiu, G, Bühmann, L, Westphal, P, Stadler, C, Ermilov, I, Chakraborty, N, Bin, S, Saleem, M, Ngonga Ngomo, AC & Jabeen, H 2017, Distributed semantic analytics using the SANSA stack. in M Fernandez, C d’Amato, V Tamma, P Cudre-Mauroux, F Lecue, C Lange, J Sequeda & J Heflin (eds), The Semantic Web – ISWC 2017 - 16th International Semantic Web Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10588 LNCS, Springer-Verlag Austria, pp. 147-155, 16th International Semantic Web Conference, ISWC 2017, Vienna, Austria, 21.10.17. https://doi.org/10.1007/978-3-319-68204-4_15

Distributed semantic analytics using the SANSA stack. / Lehmann, Jens; Sejdiu, Gezim; Bühmann, Lorenz et al.
The Semantic Web – ISWC 2017 - 16th International Semantic Web Conference, Proceedings. ed. / Miriam Fernandez; Claudia d’Amato; Valentina Tamma; Philippe Cudre-Mauroux; Freddy Lecue; Christoph Lange; Juan Sequeda; Jeff Heflin. Springer-Verlag Austria, 2017. p. 147-155 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10588 LNCS).

Research output: Contributions to collected editions/works › Article in conference proceedings › Research › peer-review

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T1 - Distributed semantic analytics using the SANSA stack

AU - Lehmann, Jens

AU - Sejdiu, Gezim

AU - Bühmann, Lorenz

AU - Westphal, Patrick

AU - Stadler, Claus

AU - Ermilov, Ivan

AU - Chakraborty, Nilesh

AU - Bin, Simon

AU - Saleem, Muhammad

AU - Ngonga Ngomo, Axel Cyrille

AU - Jabeen, Hajira

N1 - Conference code: 16

PY - 2017

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N2 - A major research challenge is to perform scalable analysis of large-scale knowledge graphs to facilitate applications like link prediction, knowledge base completion and reasoning. Analytics methods which exploit expressive structures usually do not scale well to very large knowledge bases, and most analytics approaches which do scale horizontally (i.e., can be executed in a distributed environment) work on simple feature-vector-based input. This software framework paper describes the ongoing Semantic Analytics Stack (SANSA) project, which supports expressive and scalable semantic analytics by providing functionality for distributed computing on RDF data.

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KW - Computer science

KW - Analytics

KW - Exploit

KW - Scalability

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KW - Data science

KW - Knowledge base

KW - Semantic Web

KW - Stack (abstract data type)

KW - World Wide Web

KW - Programming language

KW - Semantic computing

KW - Database

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DO - 10.1007/978-3-319-68204-4_15

M3 - Article in conference proceedings

AN - SCOPUS:85032189321

SN - 978-3-319-68203-7

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 147

EP - 155

BT - The Semantic Web – ISWC 2017 - 16th International Semantic Web Conference, Proceedings

A2 - Fernandez, Miriam

A2 - d’Amato, Claudia

A2 - Tamma, Valentina

A2 - Cudre-Mauroux, Philippe

A2 - Lecue, Freddy

A2 - Lange, Christoph

A2 - Sequeda, Juan

A2 - Heflin, Jeff

PB - Springer-Verlag Austria

T2 - 16th International Semantic Web Conference, ISWC 2017

Y2 - 21 October 2017 through 25 October 2017

ER -

Lehmann J, Sejdiu G, Bühmann L, Westphal P, Stadler C, Ermilov I et al. Distributed semantic analytics using the SANSA stack. In Fernandez M, d’Amato C, Tamma V, Cudre-Mauroux P, Lecue F, Lange C, Sequeda J, Heflin J, editors, The Semantic Web – ISWC 2017 - 16th International Semantic Web Conference, Proceedings. Springer-Verlag Austria. 2017. p. 147-155. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-68204-4_15

Distributed semantic analytics using the SANSA stack

Abstract

Bibliographical note

Research areas and keywords

ASJC Scopus Subject Areas

Access to Document

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Cite this