paper-with-me

Papers

Knowledge Graphs Evolution and Preservation -- A Technical Report from ISWS 2019

2020-12-22 · 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

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.

📄 PDF Abstract BibTeX arXiv:2012.11936

Code (0)

등록된 구현이 없습니다.

Tasks

Common Sense ReasoningKnowledge Graphs

Similar Papers 제목 키워드 기반

Spectral Toolkit of Algorithms for Graphs: Technical Report (2)

2024-06-06 · Peter Macgregor, He Sun

Spectral Toolkit of Algorithms for Graphs (STAG) is an open-source library for efficient graph algorithms. This technical report presents the newly implemented component on locality sensitive hashing, kernel density esti…

ClusteringDensity Estimation

Spectral Toolkit of Algorithms for Graphs: Technical Report (1)

2023-04-05 · Peter Macgregor, He Sun

Spectral Toolkit of Algorithms for Graphs (STAG) is an open-source library for efficient spectral graph algorithms, and its development starts in September 2022. We have so far finished the component on local graph clust…

ClusteringGraph Clustering

Column-Oriented Datalog Materialization for Large Knowledge Graphs (Extended Technical Report)

2015-11-28 · Jacopo Urbani, Ceriel Jacobs, Markus Krötzsch

The evaluation of Datalog rules over large Knowledge Graphs (KGs) is essential for many applications. In this paper, we present a new method of materializing Datalog inferences, which combines a column-based memory layou…

Knowledge Graphs

Kyrtos: A methodology for automatic deep analysis of graphic charts with curves in technical documents

2026-02-10 · Michail S. Alexiou, Nikolaos G. Bourbakis arxiv

Deep Understanding of Technical Documents (DUTD) has become a very attractive field with great potential due to large amounts of accumulated documents and the valuable knowledge contained in them. In addition, the holist…

Learning with Memory Embeddings

2015-11-25 · Volker Tresp, Cristóbal Esteban, Yinchong Yang, Stephan Baier 외

Embedding learning, a.k.a. representation learning, has been shown to be able to model large-scale semantic knowledge graphs. A key concept is a mapping of the knowledge graph to a tensor representation whose entries are…

Knowledge GraphsRepresentation Learning