TRTML - A tripleset recommendation tool based on supervised learning algorithms

Alexander Arturo Mera Caraballo; Narciso Moura Arruda; Bernardo Pereira Nunes; Giseli Rabello Lopes; Marco Antonio Casanova

doi:10.1007/978-3-319-11955-7_58

TRTML - A tripleset recommendation tool based on supervised learning algorithms

Alexander Arturo Mera Caraballo^*, Narciso Moura Arruda, Bernardo Pereira Nunes, Giseli Rabello Lopes, Marco Antonio Casanova

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

6 Citations (Scopus)

Abstract

The Linked Data initiative promotes the publication of interlinked RDF triplesets, thereby creating a global scale data space. However, to enable the creation of such data space, the publisher of a tripleset t must be aware of other triplesets that he can interlink with t. Towards this end, this paper describes a Web-based application, called TRTML, that explores metadata available in Linked Data catalogs to provide data publishers with recommendations of related triplesets. TRTML combines supervised learning algorithms and link prediction measures to provide recommendations. The evaluation of the tool adopted as ground truth a set of links obtained from metadata stored in the DataHub catalog. The high precision and recall results demonstrate the usefulness of TRTML.

Original language	English
Title of host publication	The Semantic Web
Subtitle of host publication	ESWC 2014 Satellite Events, Revised Selected Papers
Editors	Anna Tordai, Eva Blomqvist, Harald Sack, Raphaël Troncy, Valentina Presutti, Ioannis Papadakis
Publisher	Springer Verlag
Pages	413-417
Number of pages	5
ISBN (Electronic)	9783319119540
DOIs	https://doi.org/10.1007/978-3-319-11955-7_58
Publication status	Published - 2014
Externally published	Yes
Event	11th European Semantic Web Symposium on Satellite Events, ESWC 2014 - Ouro Preto, Brazil Duration: 20 Oct 2014 → 22 Oct 2014

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	8798
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	11th European Semantic Web Symposium on Satellite Events, ESWC 2014
Country/Territory	Brazil
City	Ouro Preto
Period	20/10/14 → 22/10/14

Access to Document

10.1007/978-3-319-11955-7_58

Cite this

Caraballo, A. A. M., Arruda, N. M., Nunes, B. P., Lopes, G. R., & Casanova, M. A. (2014). TRTML - A tripleset recommendation tool based on supervised learning algorithms. In A. Tordai, E. Blomqvist, H. Sack, R. Troncy, V. Presutti, & I. Papadakis (Eds.), The Semantic Web: ESWC 2014 Satellite Events, Revised Selected Papers (pp. 413-417). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8798). Springer Verlag. https://doi.org/10.1007/978-3-319-11955-7_58

Caraballo, Alexander Arturo Mera ; Arruda, Narciso Moura ; Nunes, Bernardo Pereira et al. / TRTML - A tripleset recommendation tool based on supervised learning algorithms. The Semantic Web: ESWC 2014 Satellite Events, Revised Selected Papers. editor / Anna Tordai ; Eva Blomqvist ; Harald Sack ; Raphaël Troncy ; Valentina Presutti ; Ioannis Papadakis. Springer Verlag, 2014. pp. 413-417 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{ddc17df9a0be430c9c69f6428d6a1c6f,

title = "TRTML - A tripleset recommendation tool based on supervised learning algorithms",

abstract = "The Linked Data initiative promotes the publication of interlinked RDF triplesets, thereby creating a global scale data space. However, to enable the creation of such data space, the publisher of a tripleset t must be aware of other triplesets that he can interlink with t. Towards this end, this paper describes a Web-based application, called TRTML, that explores metadata available in Linked Data catalogs to provide data publishers with recommendations of related triplesets. TRTML combines supervised learning algorithms and link prediction measures to provide recommendations. The evaluation of the tool adopted as ground truth a set of links obtained from metadata stored in the DataHub catalog. The high precision and recall results demonstrate the usefulness of TRTML.",

keywords = "Link prediction, Linked data, Machine learning, Recommender systems",

author = "Caraballo, {Alexander Arturo Mera} and Arruda, {Narciso Moura} and Nunes, {Bernardo Pereira} and Lopes, {Giseli Rabello} and Casanova, {Marco Antonio}",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2014.; 11th European Semantic Web Symposium on Satellite Events, ESWC 2014 ; Conference date: 20-10-2014 Through 22-10-2014",

year = "2014",

doi = "10.1007/978-3-319-11955-7_58",

language = "English",

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

publisher = "Springer Verlag",

pages = "413--417",

editor = "Anna Tordai and Eva Blomqvist and Harald Sack and Rapha{\"e}l Troncy and Valentina Presutti and Ioannis Papadakis",

booktitle = "The Semantic Web",

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Caraballo, AAM, Arruda, NM, Nunes, BP, Lopes, GR & Casanova, MA 2014, TRTML - A tripleset recommendation tool based on supervised learning algorithms. in A Tordai, E Blomqvist, H Sack, R Troncy, V Presutti & I Papadakis (eds), The Semantic Web: ESWC 2014 Satellite Events, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8798, Springer Verlag, pp. 413-417, 11th European Semantic Web Symposium on Satellite Events, ESWC 2014, Ouro Preto, Brazil, 20/10/14. https://doi.org/10.1007/978-3-319-11955-7_58

TRTML - A tripleset recommendation tool based on supervised learning algorithms. / Caraballo, Alexander Arturo Mera; Arruda, Narciso Moura; Nunes, Bernardo Pereira et al.
The Semantic Web: ESWC 2014 Satellite Events, Revised Selected Papers. ed. / Anna Tordai; Eva Blomqvist; Harald Sack; Raphaël Troncy; Valentina Presutti; Ioannis Papadakis. Springer Verlag, 2014. p. 413-417 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8798).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - TRTML - A tripleset recommendation tool based on supervised learning algorithms

AU - Caraballo, Alexander Arturo Mera

AU - Arruda, Narciso Moura

AU - Nunes, Bernardo Pereira

AU - Lopes, Giseli Rabello

AU - Casanova, Marco Antonio

N1 - Publisher Copyright: © Springer International Publishing Switzerland 2014.

PY - 2014

Y1 - 2014

N2 - The Linked Data initiative promotes the publication of interlinked RDF triplesets, thereby creating a global scale data space. However, to enable the creation of such data space, the publisher of a tripleset t must be aware of other triplesets that he can interlink with t. Towards this end, this paper describes a Web-based application, called TRTML, that explores metadata available in Linked Data catalogs to provide data publishers with recommendations of related triplesets. TRTML combines supervised learning algorithms and link prediction measures to provide recommendations. The evaluation of the tool adopted as ground truth a set of links obtained from metadata stored in the DataHub catalog. The high precision and recall results demonstrate the usefulness of TRTML.

AB - The Linked Data initiative promotes the publication of interlinked RDF triplesets, thereby creating a global scale data space. However, to enable the creation of such data space, the publisher of a tripleset t must be aware of other triplesets that he can interlink with t. Towards this end, this paper describes a Web-based application, called TRTML, that explores metadata available in Linked Data catalogs to provide data publishers with recommendations of related triplesets. TRTML combines supervised learning algorithms and link prediction measures to provide recommendations. The evaluation of the tool adopted as ground truth a set of links obtained from metadata stored in the DataHub catalog. The high precision and recall results demonstrate the usefulness of TRTML.

KW - Link prediction

KW - Linked data

KW - Machine learning

KW - Recommender systems

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U2 - 10.1007/978-3-319-11955-7_58

DO - 10.1007/978-3-319-11955-7_58

M3 - Conference contribution

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

SP - 413

EP - 417

BT - The Semantic Web

A2 - Tordai, Anna

A2 - Blomqvist, Eva

A2 - Sack, Harald

A2 - Troncy, Raphaël

A2 - Presutti, Valentina

A2 - Papadakis, Ioannis

PB - Springer Verlag

T2 - 11th European Semantic Web Symposium on Satellite Events, ESWC 2014

Y2 - 20 October 2014 through 22 October 2014

ER -

Caraballo AAM, Arruda NM, Nunes BP, Lopes GR, Casanova MA. TRTML - A tripleset recommendation tool based on supervised learning algorithms. In Tordai A, Blomqvist E, Sack H, Troncy R, Presutti V, Papadakis I, editors, The Semantic Web: ESWC 2014 Satellite Events, Revised Selected Papers. Springer Verlag. 2014. p. 413-417. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-11955-7_58

TRTML - A tripleset recommendation tool based on supervised learning algorithms

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