Ali Assi

Orcid: 0000-0003-3709-1519

Affiliations:
  • Rochester Institute of Technology - Dubai, Dubai, UAE
  • The House of Commons, Ottawa, Canada
  • University of Quebec at Montreal, Quebec, Canada


According to our database1, Ali Assi authored at least 11 papers between 2019 and 2026.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

Book  In proceedings  Article  PhD thesis  Dataset  Other 

Links

Online presence:

On csauthors.net:

Bibliography

2026
Towards better random forests with tree weighting, accuracy and diversity-preserving pruning.
Expert Syst. Appl., 2026

2025
A genetic and graph-guided feature learning strategy for improving decision tree construction.
Clust. Comput., September, 2025

Learning Global-Local Multi-Scale Node Embeddings with Random Walks and Landmark-Guided Optimization.
Proceedings of the 34th ACM International Conference on Information and Knowledge Management, 2025

2023
Accuracy and diversity-aware multi-objective approach for random forest construction.
Expert Syst. Appl., September, 2023

2022
Scoring Ontologies for Reuse: An Approach for Fitting Semantic Requirements.
Proceedings of the Metadata and Semantic Research - 16th Research Conference, 2022

2021
Instance Matching in Knowledge Graphs through random walks and semantics.
Future Gener. Comput. Syst., 2021

2020
Data linking over RDF knowledge graphs: A survey.
Concurr. Comput. Pract. Exp., 2020

Instance Matching in Knowledge Graphs Through Dynamic, Distributed and Affinity-Preserving Random Walk.
Proceedings of the 2020 IEEE International Conference on Big Data (IEEE BigData 2020), 2020

2019
Context-aware instance matching through graph embedding in lexical semantic space.
Knowl. Based Syst., 2019

Context-Aware Instance Matching Through Graph Embedding in Lexical Semantic Space.
Proceedings of the Advances and Trends in Artificial Intelligence. From Theory to Practice, 2019

BIGMAT: A Distributed Affinity-Preserving Random Walk Strategy for Instance Matching on Knowledge Graphs.
Proceedings of the 2019 IEEE International Conference on Big Data (IEEE BigData), 2019


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