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Linked Open Data Validity -- A Technical Report from ISWS 2018

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arxiv 1903.12554 v1 pith:G5ERMLLV submitted 2019-03-26 cs.DB cs.CY

Linked Open Data Validity -- A Technical Report from ISWS 2018

Tayeb Abderrahmani Ghor , Esha Agrawal , Mehwish Alam , Omar Alqawasmeh , Claudia D'amato , Amina Annane , Amr Azzam , Andrew Berezovskyi
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Russa Biswas Mathias Bonduel Quentin Brabant Cristina-iulia Bucur Elena Camossi Valentina Anita Carriero Shruthi Chari David Chaves Fraga Fiorela Ciroku Michael Cochez Hubert Curien Vincenzo Cutrona Rahma Dandan Danilo Dess Valerio Di Carlo Ahmed El Amine Djebri Marieke Van Erp Faiq Miftakhul Falakh Alba Fernndez Izquierdo Giuseppe Futia Aldo Gangemi Simone Gasperoni Arnaud Grall Lars Heling Pierre Henri Noura Herradi Subhi Issa Samaneh Jozashoori Nyoman Juniarta Lucie-aime Kaffee Ilkcan Keles Prashant Khare Viktor Kovtun Valentina Leone Siying Li Sven Lieber Pasquale Lisena Tatiana Makhalova Ludovica Marinucci Thomas Minier Benjamin Moreau Alberto Moya Loustaunau Durgesh Nandini Sylwia Ozdowska Amanda Pacini De Moura Swati Padhee Guillermo Palma Pedro Del Pozo Jimnez Valentina Presutti Roberto Reda Ettore Rizza Henry Rosales-mndez Sebastian Rudolph Harald Sack Luca Sciullo Humasak Simanjuntak Carlo Stomeo Thiviyan Thanapalasingam Tabea Tietz Dalia Varanka Maria-esther Vidal Michael Wolowyk Maximilian Zocholl
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classification cs.DB cs.CY
keywords datalinkedknowledgeavailabledifferentdiverseiswsopen
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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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.

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