R
Reynold Cheng
Researcher at University of Hong Kong
Publications - 192
Citations - 8947
Reynold Cheng is an academic researcher from University of Hong Kong. The author has contributed to research in topics: Uncertain data & Probabilistic logic. The author has an hindex of 44, co-authored 188 publications receiving 7717 citations. Previous affiliations of Reynold Cheng include University of New South Wales & Hong Kong Polytechnic University.
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Journal ArticleDOI
Drug Repurposing for the Treatment of COVID-19: A Knowledge Graph Approach
Vincent K.C. Yan,Xiaodong Li,Xuxiao Ye,Min Ou,Ruibang Luo,Qingpeng Zhang,Bo Tang,Benjamin J. Cowling,Ivan Hung,Chung-Wah Siu,Ian C. K. Wong,Reynold Cheng,Esther W. Chan +12 more
TL;DR: In this paper, a COVID-19 knowledge graph by integrating 14 public bioinformatic databases containing information on drugs, genes, proteins, viruses, diseases, symptoms and their linkages is developed.
Journal ArticleDOI
SMe: explicit & implicit constrained-space probabilistic threshold range queries for moving objects
TL;DR: The central idea is to swap the order of geometric operations and to compute the appearance probability in a multi-step manner and to differentiate two forms of CSPTRQs: explicit and implicit ones.
Proceedings ArticleDOI
SpaceKey: Exploring Patterns in Spatial Databases
TL;DR: This paper proposes SpaceKey, a system for retrieving and visualizing spatial objects returned by SGK queries, and supports a novel query, called SPM query, which is defined by a spatial pattern, a graph whose vertices contain keywords and its edges are associated with distance constraints.
Proceedings ArticleDOI
Evaluating Top-k Meta Path Queries on Large Heterogeneous Information Networks
TL;DR: A solution that seamlessly integrates several ranking functions that evaluate the importance of meta paths based on frequency and rarity, rather than on path length is proposed that outperforms state-of-the-art algorithms.
Proceedings ArticleDOI
An Efficient Framework for Correctness-Aware kNN Queries on Road Networks
TL;DR: A framework on correctness-aware kNN queries which aim to optimize system throughput while guaranteeing query correctness on moving objects, and formally defines the serializable-kNN query that ensures the correctness of the query answer when considering moving objects and dependencies of different queries.