Linked Open Data Validity: A Technical Report from ISWS 2018

Marieke Van Erp

Linked Open Data Validity: A Technical Report from ISWS 2018

DHLab

Onderzoeksoutput: Boek/Rapport › Rapport › Wetenschappelijk

Samenvatting

Linked Open Data (LOD) is the publicly available RDF data in the Web. Each LOD entity is identfied by a URI and accessible via HTTP. LOD encodes globalscale knowledge potentially available to any human as well as artificial intelligence that may want to benefit from it as background knowledge for supporting their tasks. LOD has emerged as the backbone of applications in diverse fields such as Natural Language Processing, Information Retrieval, Computer Vision, Speech Recognition, and many more. Nevertheless, regardless of the specific tasks that LOD-based tools aim to address, the reuse of such knowledge may be challenging for diverse reasons, e.g. semantic heterogeneity, provenance, and data quality. As aptly stated by Heath et al. Linked Data might be outdated, imprecise, or simply wrong": there arouses a necessity to investigate the problem of linked data validity. This work reports a collaborative effort performed by nine teams of students, guided by an equal number of senior researchers, attending the International Semantic Web Research School (ISWS 2018) towards addressing such investigation from different perspectives coupled with different approaches to tackle the issue.

Originele taal-2	Engels
Status	Gepubliceerd - 26 mrt. 2019

Toegang tot document

https://arxiv.org/abs/1903.12554

Citeer dit

@book{e744597f2c44436e93c04d3a7588fddb,

title = "Linked Open Data Validity: A Technical Report from ISWS 2018",

abstract = " Linked Open Data (LOD) is the publicly available RDF data in the Web. Each LOD entity is identfied by a URI and accessible via HTTP. LOD encodes globalscale knowledge potentially available to any human as well as artificial intelligence that may want to benefit from it as background knowledge for supporting their tasks. LOD has emerged as the backbone of applications in diverse fields such as Natural Language Processing, Information Retrieval, Computer Vision, Speech Recognition, and many more. Nevertheless, regardless of the specific tasks that LOD-based tools aim to address, the reuse of such knowledge may be challenging for diverse reasons, e.g. semantic heterogeneity, provenance, and data quality. As aptly stated by Heath et al. Linked Data might be outdated, imprecise, or simply wrong{"}: there arouses a necessity to investigate the problem of linked data validity. This work reports a collaborative effort performed by nine teams of students, guided by an equal number of senior researchers, attending the International Semantic Web Research School (ISWS 2018) towards addressing such investigation from different perspectives coupled with different approaches to tackle the issue. ",

keywords = "cs.DB, cs.CY",

author = "Ghor, {Tayeb Abderrahmani} and Esha Agrawal and Mehwish Alam and Omar Alqawasmeh and Claudia D'amato and Amina Annane and Amr Azzam and Andrew Berezovskyi and Russa Biswas and Mathias Bonduel and Quentin Brabant and Cristina-iulia Bucur and Elena Camossi and Carriero, {Valentina Anita} and Shruthi Chari and Fraga, {David Chaves} and Fiorela Ciroku and Michael Cochez and Hubert Curien and Vincenzo Cutrona and Rahma Dandan and Danilo Dess and Carlo, {Valerio Di} and Djebri, {Ahmed El Amine} and Erp, {Marieke Van} and Falakh, {Faiq Miftakhul} and Izquierdo, {Alba Fernndez} and Giuseppe Futia and Aldo Gangemi and Simone Gasperoni and Arnaud Grall and Lars Heling and Pierre Henri and Noura Herradi and Subhi Issa and Samaneh Jozashoori and Nyoman Juniarta and Lucie-aime Kaffee and Ilkcan Keles and Prashant Khare and Viktor Kovtun and Valentina Leone and Siying Li and Sven Lieber and Pasquale Lisena and Tatiana Makhalova and Ludovica Marinucci and Thomas Minier and Benjamin Moreau and Loustaunau, {Alberto Moya}",

year = "2019",

month = mar,

day = "26",

language = "English",

}

TY - BOOK

T1 - Linked Open Data Validity

T2 - A Technical Report from ISWS 2018

AU - Ghor, Tayeb Abderrahmani

AU - Agrawal, Esha

AU - Alam, Mehwish

AU - Alqawasmeh, Omar

AU - D'amato, Claudia

AU - Annane, Amina

AU - Azzam, Amr

AU - Berezovskyi, Andrew

AU - Biswas, Russa

AU - Bonduel, Mathias

AU - Brabant, Quentin

AU - Bucur, Cristina-iulia

AU - Camossi, Elena

AU - Carriero, Valentina Anita

AU - Chari, Shruthi

AU - Fraga, David Chaves

AU - Ciroku, Fiorela

AU - Cochez, Michael

AU - Curien, Hubert

AU - Cutrona, Vincenzo

AU - Dandan, Rahma

AU - Dess, Danilo

AU - Carlo, Valerio Di

AU - Djebri, Ahmed El Amine

AU - Erp, Marieke Van

AU - Falakh, Faiq Miftakhul

AU - Izquierdo, Alba Fernndez

AU - Futia, Giuseppe

AU - Gangemi, Aldo

AU - Gasperoni, Simone

AU - Grall, Arnaud

AU - Heling, Lars

AU - Henri, Pierre

AU - Herradi, Noura

AU - Issa, Subhi

AU - Jozashoori, Samaneh

AU - Juniarta, Nyoman

AU - Kaffee, Lucie-aime

AU - Keles, Ilkcan

AU - Khare, Prashant

AU - Kovtun, Viktor

AU - Leone, Valentina

AU - Li, Siying

AU - Lieber, Sven

AU - Lisena, Pasquale

AU - Makhalova, Tatiana

AU - Marinucci, Ludovica

AU - Minier, Thomas

AU - Moreau, Benjamin

AU - Loustaunau, Alberto Moya

PY - 2019/3/26

Y1 - 2019/3/26

N2 - Linked Open Data (LOD) is the publicly available RDF data in the Web. Each LOD entity is identfied by a URI and accessible via HTTP. LOD encodes globalscale knowledge potentially available to any human as well as artificial intelligence that may want to benefit from it as background knowledge for supporting their tasks. LOD has emerged as the backbone of applications in diverse fields such as Natural Language Processing, Information Retrieval, Computer Vision, Speech Recognition, and many more. Nevertheless, regardless of the specific tasks that LOD-based tools aim to address, the reuse of such knowledge may be challenging for diverse reasons, e.g. semantic heterogeneity, provenance, and data quality. As aptly stated by Heath et al. Linked Data might be outdated, imprecise, or simply wrong": there arouses a necessity to investigate the problem of linked data validity. This work reports a collaborative effort performed by nine teams of students, guided by an equal number of senior researchers, attending the International Semantic Web Research School (ISWS 2018) towards addressing such investigation from different perspectives coupled with different approaches to tackle the issue.

AB - Linked Open Data (LOD) is the publicly available RDF data in the Web. Each LOD entity is identfied by a URI and accessible via HTTP. LOD encodes globalscale knowledge potentially available to any human as well as artificial intelligence that may want to benefit from it as background knowledge for supporting their tasks. LOD has emerged as the backbone of applications in diverse fields such as Natural Language Processing, Information Retrieval, Computer Vision, Speech Recognition, and many more. Nevertheless, regardless of the specific tasks that LOD-based tools aim to address, the reuse of such knowledge may be challenging for diverse reasons, e.g. semantic heterogeneity, provenance, and data quality. As aptly stated by Heath et al. Linked Data might be outdated, imprecise, or simply wrong": there arouses a necessity to investigate the problem of linked data validity. This work reports a collaborative effort performed by nine teams of students, guided by an equal number of senior researchers, attending the International Semantic Web Research School (ISWS 2018) towards addressing such investigation from different perspectives coupled with different approaches to tackle the issue.

KW - cs.DB

KW - cs.CY

M3 - Report

BT - Linked Open Data Validity

ER -

Linked Open Data Validity: A Technical Report from ISWS 2018

Samenvatting

Toegang tot document

Vingerafdruk

Citeer dit