Linkitup: Link discovery for research data

Rinke Hoekstra; Paul Groth

Linkitup: Link discovery for research data

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

Abstract

Linkitup is a Web-based dashboard for enrichment of research output published via industry grade data repository services. It takes metadata entered through Figshare.com and tries to find equivalent terms, categories, persons or entities on the Linked Data cloud and several Web 2.0 services. It extracts references from publications, and tries to find the corresponding Digital Object Identifier (DOI). Linkitup feeds the enriched metadata back as links to the original article in the repository, but also builds a RDF representation of the metadata that can be downloaded separately, or published as research output in its own right. In this paper, we compare Linkitup to the standard workflow of publishing linked data, and show that it significantly lowers the threshold for publishing linked research data.

Original language	English
Title of host publication	Discovery Informatics
Subtitle of host publication	AI Takes a Science-Centered View on Big Data - Papers from the AAAI Fall Symposium, Technical Report
Publisher	AI Access Foundation
Pages	28-35
Number of pages	8
ISBN (Print)	9781577356394
State	Published - 2013
Externally published	Yes
Event	2013 AAAI Fall Symposium - Arlington, VA, United States Duration: Nov 15 2013 → Nov 17 2013

Publication series

Name	AAAI Fall Symposium - Technical Report
Volume	FS-13-01

Conference

Conference	2013 AAAI Fall Symposium
Country/Territory	United States
City	Arlington, VA
Period	11/15/13 → 11/17/13

Cite this

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title = "Linkitup: Link discovery for research data",

abstract = "Linkitup is a Web-based dashboard for enrichment of research output published via industry grade data repository services. It takes metadata entered through Figshare.com and tries to find equivalent terms, categories, persons or entities on the Linked Data cloud and several Web 2.0 services. It extracts references from publications, and tries to find the corresponding Digital Object Identifier (DOI). Linkitup feeds the enriched metadata back as links to the original article in the repository, but also builds a RDF representation of the metadata that can be downloaded separately, or published as research output in its own right. In this paper, we compare Linkitup to the standard workflow of publishing linked data, and show that it significantly lowers the threshold for publishing linked research data.",

author = "Rinke Hoekstra and Paul Groth",

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isbn = "9781577356394",

series = "AAAI Fall Symposium - Technical Report",

publisher = "AI Access Foundation",

pages = "28--35",

booktitle = "Discovery Informatics",

note = "2013 AAAI Fall Symposium ; Conference date: 15-11-2013 Through 17-11-2013",

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Linkitup: Link discovery for research data. / Hoekstra, Rinke ; Groth, Paul.
Discovery Informatics: AI Takes a Science-Centered View on Big Data - Papers from the AAAI Fall Symposium, Technical Report. AI Access Foundation, 2013. p. 28-35 (AAAI Fall Symposium - Technical Report; Vol. FS-13-01).

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

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AB - Linkitup is a Web-based dashboard for enrichment of research output published via industry grade data repository services. It takes metadata entered through Figshare.com and tries to find equivalent terms, categories, persons or entities on the Linked Data cloud and several Web 2.0 services. It extracts references from publications, and tries to find the corresponding Digital Object Identifier (DOI). Linkitup feeds the enriched metadata back as links to the original article in the repository, but also builds a RDF representation of the metadata that can be downloaded separately, or published as research output in its own right. In this paper, we compare Linkitup to the standard workflow of publishing linked data, and show that it significantly lowers the threshold for publishing linked research data.

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M3 - Contribución a la conferencia

AN - SCOPUS:84898926852

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SP - 28

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BT - Discovery Informatics

PB - AI Access Foundation

Y2 - 15 November 2013 through 17 November 2013

ER -

Linkitup: Link discovery for research data

Abstract

Publication series

Conference

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