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arXiv 2012.11936cs.AI
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Knowledge Graphs Evolution and Preservation -- A Technical Report from ISWS 2019

ISTC-CNR(意大利国家研究委员会认知科学与技术研究所);FIZ Karlsruhe(卡尔斯鲁厄专业信息中心);University of Bari(巴里大学);Cefriel(切夫里尔研究中心);Open University(开放大学);Maastricht University(马斯特里赫特大学);KNAW Humanities Cluster(荷兰皇家艺术与科学院人文集群);University of Mannheim(曼海姆大学);Vienna University of Economics and Business(维也纳经济大学);TU Dresden(德累斯顿工业大学);INRIA(法国国家信息与自动化研究所);The University of Southampton(南安普顿大学);University of Genoa(热那亚大学);University of Torino(都灵大学);CELI - Language Technology(CELI语言技术公司);Free University of Bozen-Bolzano(博尔扎诺自由大学);GESIS - Leibniz Institute for the Social Sciences(GESIS莱布尼茨社会科学研究所);University of California, Santa Barbara(加州大学圣巴巴拉分校);Linköping University(林雪平大学);Eurecom(欧洲通信研究院);University of Bologna(博洛尼亚大学);Ghent University(根特大学);imec(比利时微电子研究中心);Institute of Data Science, Maastricht University(马斯特里赫特大学数据科学研究所);Universidad Politécnica de Madrid(马德里理工大学);University of Sheffield(谢菲尔德大学);University of Chile(智利大学);University of Bergen(卑尔根大学);University of Huddersfield(哈德斯菲尔德大学)
Nacira Abbas, Kholoud Alghamdi, Mortaza Alinam, Francesca Alloatti, Glenda Amaral, Claudia d'Amato, Luigi Asprino, Martin Beno, Felix Bensmann, Russa Biswas, Li… 展开作者

Nacira Abbas, Kholoud Alghamdi, Mortaza Alinam, Francesca Alloatti, Glenda Amaral, Claudia d'Amato, Luigi Asprino, Martin Beno, Felix Bensmann, Russa Biswas, Ling Cai, Riley Capshaw, Valentina Anita Carriero, Irene Celino, Amine Dadoun, Stefano De Giorgis, Harm Delva, John Domingue, Michel Dumontier, Vincent Emonet, Marieke van Erp, Paola Espinoza Arias, Omaima Fallatah, Sebastián Ferrada, Marc Gallofré Ocaña, Michalis Georgiou, Genet Asefa Gesese, Frances Gillis-Webber, Francesca Giovannetti, Marìa Granados Buey, Ismail Harrando, Ivan Heibi, Vitor Horta, Laurine Huber, Federico Igne, Mohamad Yaser Jaradeh, Neha Keshan, Aneta Koleva, Bilal Koteich, Kabul Kurniawan, Mengya Liu, Chuangtao Ma, Lientje Maas, Martin Mansfield, Fabio Mariani, Eleonora Marzi, Sepideh Mesbah, Maheshkumar Mistry, Alba Catalina Morales Tirado, Anna Nguyen, Viet Bach Nguyen, Allard Oelen, Valentina Pasqual, Heiko Paulheim, Axel Polleres, Margherita Porena, Jan Portisch, Valentina Presutti, Kader Pustu-Iren, Ariam Rivas Mendez, Soheil Roshankish, Sebastian Rudolph, Harald Sack, Ahmad Sakor, Jaime Salas, Thomas Schleider, Meilin Shi, Gianmarco Spinaci, Chang Sun, Tabea Tietz, Molka Tounsi Dhouib, Alessandro Umbrico, Wouter van den Berg, Weiqin Xu

英文摘要:

One of the grand challenges discussed during the Dagstuhl Seminar "Knowledge Graphs: New Directions for Knowledge Representation on the Semantic Web" and described in its report is that of a: "Public FAIR Knowledge Graph of Everything: We increasingly see the creation of knowledge graphs that capture information about the entirety of a class of entities. [...] This grand challenge extends this further by asking if we can create a knowledge graph of "everything" ranging from common sense concepts to location based entities. This knowledge graph should be "open to the public" in a FAIR manner democratizing this mass amount of knowledge." Although linked open data (LOD) is one knowledge graph, it is the closest realisation (and probably the only one) to a public FAIR Knowledge Graph (KG) of everything. Surely, LOD provides a unique testbed for experimenting and evaluating research hypotheses on open and FAIR KG. One of the most neglected FAIR issues about KGs is their ongoing evolution and long term preservation. We want to investigate this problem, that is to understand what preserving and supporting the evolution of KGs means and how these problems can be addressed. Clearly, the problem can be approached from different perspectives and may require the development of different approaches, including new theories, ontologies, metrics, strategies, procedures, etc. This document reports a collaborative effort performed by 9 teams of students, each guided by a senior researcher as their mentor, attending the International Semantic Web Research School (ISWS 2019). Each team provides a different perspective to the problem of knowledge graph evolution substantiated by a set of research questions as the main subject of their investigation. In addition, they provide their working definition for KG preservation and evolution.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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