1 citations · 1 across the 10 of their papers we have counts for
10 papers
RAE: A Rule-Driven Approach for Attribute Embedding in Property Graph Recommendation
Sibo Zhao, Michael Bewong, Selasi Kwashie +2
Recommendation systems are crucial in modern applications to enhance the user experience and drive business conversion rates through personalization. However, insufficient utilizat…
FastER: On-Demand Entity Resolution in Property Graphs
Shujing Wang, Sibo Zhao, Shiqi Miao +5
Entity resolution (ER) is the problem of identifying and linking database records that refer to the same real-world entity. Traditional ER methods use batch processing, which becom…
GIG: Graph Data Imputation With Graph Differential Dependencies
Jiang Hua, Michael Bewong, Selasi Kwashie +4
Data imputation addresses the challenge of imputing missing values in database instances, ensuring consistency with the overall semantics of the dataset. Although several heuristic…
A Heterogeneous Network-based Contrastive Learning Approach for Predicting Drug-Target Interaction
Junwei Hu, Michael Bewong, Selasi Kwashie +4
Drug-target interaction (DTI) prediction is crucial for drug development and repositioning. Methods using heterogeneous graph neural networks (HGNNs) for DTI prediction have become…
When GDD meets GNN: A Knowledge-driven Neural Connection for Effective Entity Resolution in Property Graphs
Junwei Hu, Michael Bewong, Selasi Kwashie +4
This paper studies the entity resolution (ER) problem in property graphs. ER is the task of identifying and linking different records that refer to the same real-world entity. It i…
Is it Still Fair? A Comparative Evaluation of Fairness Algorithms through the Lens of Covariate Drift
Oscar Blessed Deho, Michael Bewong, Selasi Kwashie +4
Over the last few decades, machine learning (ML) applications have grown exponentially, yielding several benefits to society. However, these benefits are tempered with concerns of…
FASTAGEDS: Fast Approximate Graph Entity Dependency Discovery
Guangtong Zhou, Selasi Kwashie, Yidi Zhang +5
This paper studies the discovery of approximate rules in property graphs. We propose a semantically meaningful measure of error for mining graph entity dependencies (GEDs) at almos…
OutCenTR: A novel semi-supervised framework for predicting exploits of vulnerabilities in high-dimensional datasets
Hadi Eskandari, Michael Bewong, Sabih ur Rehman
An ever-growing number of vulnerabilities are reported every day. Yet these vulnerabilities are not all the same; Some are more targeted than others. Correctly estimating the likel…
An Efficient Approach for Discovering Graph Entity Dependencies (GEDs)
Dehua Liu, Selasi Kwashie, Yidi Zhang +6
Graph entity dependencies (GEDs) are novel graph constraints, unifying keys and functional dependencies, for property graphs. They have been found useful in many real-world data qu…
Linking Graph Entities with Multiplicity and Provenance
Jixue Liu, Selasi Kwashie, Jiuyong Li +2
Entity linking and resolution is a fundamental database problem with applications in data integration, data cleansing, information retrieval, knowledge fusion, and knowledge-base p…