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Anisa Rula
Researcher at University of Milano-Bicocca
Publications - 43
Citations - 1771
Anisa Rula is an academic researcher from University of Milano-Bicocca. The author has contributed to research in topics: Linked data & Data quality. The author has an hindex of 12, co-authored 35 publications receiving 969 citations. Previous affiliations of Anisa Rula include University of Bonn & University of Milan.
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Knowledge Graphs
Aidan Hogan,Eva Blomqvist,Michael Cochez,Claudia d'Amato,Gerard de Melo,Claudio Gutierrez,José Emilio Labra Gayo,Sabrina Kirrane,Sebastian Neumaier,Axel Polleres,Roberto Navigli,Axel-Cyrille Ngonga Ngomo,Sabbir M. Rashid,Anisa Rula,Lukas Schmelzeisen,Juan F. Sequeda,Steffen Staab,Antoine Zimmermann +17 more
TL;DR: The historical events that lead to the interweaving of data and knowledge are tracked to help improve knowledge and understanding of the world around us.
Journal ArticleDOI
Quality assessment for Linked Data: A Survey
TL;DR: A systematic review of approaches for assessing the quality of Linked Data, which unify and formalize commonly used terminologies across papers related to data quality and provides a comprehensive list of 18 quality dimensions and 69 metrics.
Journal ArticleDOI
Knowledge Graphs
Aidan Hogan,Eva Blomqvist,Michael Cochez,Claudia d'Amato,Gerard de Melo,Claudio Gutierrez,Sabrina Kirrane,José Emilio Labra Gayo,Roberto Navigli,Sebastian Neumaier,Axel-Cyrille Ngonga Ngomo,Axel Polleres,Sabbir M. Rashid,Anisa Rula,Lukas Schmelzeisen,Juan F. Sequeda,Steffen Staab,Antoine Zimmermann +17 more
TL;DR: In this paper, the authors provide a comprehensive introduction to knowledge graphs, which have recently garnered significant attention from both industry and academia in scenarios that require exploiting diverse, dynamic, large-scale collections of data.
Journal ArticleDOI
From Data Quality to Big Data Quality
TL;DR: The nature of the relationship between Data Quality and several research coordinates that are relevant in Big Data, such as the variety of data types, data sources and application domains, are examined, focusing on maps, semi-structured texts, linked open data, sensor & sensor networks and official statistics.
Quality Assessment Methodologies for Linked Open Data A Systematic Literature Review and Conceptual Framework
TL;DR: A systematic review of approaches for assessing the data quality of LOD is presented and a comprehensive list of the dimensions and metrics is presented to provide researchers and data curators a comprehensive understanding of existing work.