GraphML Lab:
Connecting Knowledge, Powering Intelligence

Exploring the synergy between knowledge graphs and machine learning to create more transparent, scalable, and impactful AI systems

GraphML Research Lab

GraphML lab focuses on the intersection of knowledge graphs and machine learning. It creates methods and tools that enhance interpretability, scalability, and practical applications across domains

01 Design research methods

We design hybrid approaches that leverage the structured reasoning of knowledge graphs with the predictive power of machine learning.

02 Explainable AI

We foster innovations in areas such as explainable AI, natural language understanding, and data integration.

03 Practical applications of KG+ML

We contribute to both fundamental research and real-world impact.

Research

Our research focuses on intersection of knowledge graphs and machine learning.

Knowledge Graphs in Healthcare

Knowledge graphs can be used in healthcare to organize and connect vast, disparate data, from patient records and genetic information to medical data.

Clinical Decision Support

Drug Discovery and Repurposing

Precision Medicine

Medical Research and Hypothesis Generation

Explainable AI

Explainable AI (XAI) is an emerging field focused on making AI models more transparent and understandable, so that humans can comprehend how they arrive at their decisions.

Trust in decisions

Identify model bias

Better debugging auditing

Graph-based ML

Graph-based machine learning is a field of artificial intelligence that applies machine learning algorithms directly to data structured as graphs, leveraging the relationships and connections between entities to uncover complex patterns and make predictions.

Graph-based machine learning

Knowledge-enhanced AI

Interpretable models

Research Group

Our research group focuses on advancing knowledge graphs and machine learning to develop innovative solutions for complex data-driven challenges

Somayeh Kafaie

Assistant Professor

Saint Mary's University

Enayat Rajabi

Associate Professor

Cape Breton University

Fatemeh Bagheri

Postdoctoral Fellow

Saint Mary's University

Majid Ziaratban

Research Visitor

Golestan University, Iran

Akhil Chaudhary

Researcher

Cape Breton University

Geethu Ebby

Phd Student - Saint Mary's University

Personalzied Knowledge Graph

Mohadeseh (Mahsa) Akhavanfard

Master Student - Saint Mary's University

Drug-drug Interactions with Knowledge Graphs

Naeem Shirmohammady

Master Student - Saint Mary's University

Drug-drug Interactions

Suleman Malik

Master Student - Saint Mary's University

AI-Powered Insights for Polypharmacy and Pharmaceutical Complexes

Ahmad Chowdhury

Research Assistant - Saint Mary's University

Machine Learning for Optimized Lung SBRT Dose Prescriptions

Yilin Huang

Undergraduate Student - Saint Mary's University

Utilizing LLMs to translate natural language to Cypher queries

Vijaya Laxmi Boddu

Research Assistant - Cape Breton University

Knowledge-graph-based Chatbots

Shilpa Rajan

Research Assistant - Cape Breton University

Personalzied knowledge graphs

Publications

Our publications reflect the lab’s commitment to advancing the integration of knowledge graphs and machine learning for explainable, scalable, and impactful AI. Each work contributes to bridging theory and practice, offering both fundamental insights and real-world applications.

Journal Publications

Dehal R.S., Sharma M. & Rajabi E. Knowledge Graphs and Their Reciprocal Relationship with Large Language Models
Machine Learning and Knowledge Extraction, 2025, 7 (2), 38.
Tanha M. & Kafaie S. A Review of Explainable AI for Android Malware Detection and Analysis
IEEE Access, 2025, Vol. 13, pp. 141958–141974 [pdf].
Chaudhary A., Rajabi E., Kafaie S. & Milios E. Fact Retrieval from Knowledge Graphs through Semantic and Contextual Attention
Expert Systems with Applications, 2025, Vol. 282, 127612 [pdf].
Shah Y. & Kafaie S. Evaluating Sequence Alignment Tools for Antimicrobial Resistance Gene Detection in Assembly Graphs
Microorganisms, 2024, Vol. 12, No. 11 [pdf].
Rajabi E. & Etminani K. Knowledge-Graph-Based Explainable AI: A Systematic Review
Journal of Information Science, 2024, pp. 1019–1029.
Rajabi E. & Kafaie S. Knowledge Graphs and Explainable AI in Healthcare
Information, 2022, Vol. 13, No. 10, pp. 459–468 [pdf].

Conference Publications

Akhavan Fard M., Kafaie S. & Rajabi E. DDI Prediction Using Patient Demographics in Knowledge Graphs
Artificial Intelligence 4 Knowledge Acquisition & Management workshop at the IJCAI Conference, 2025.
Ebby G., Rajabi E. & Kafaie S. Transforming Healthcare with Knowledge Graphs: Applications, Challenges, and Future Directions
Atlantic Schools of Business Conference (ASB), Halifax, Canada, 2024.
Shirmohammady N. & Kafaie S. Uncovering High-Order Drug-drug Interactions with Machine Learning
Atlantic Schools of Business Conference (ASB), Halifax, Canada, 2024
Krishnan A., Kazeem Y., Kafaie S. & Rajabi E. Antibiotic Resistance Genes Prediction Using Knowledge Graphs
Atlantic Schools of Business Conference (ASB), Halifax, Canada, 2024
Rajabi E. & Kafaie S. Building a Disease Knowledge Graph
Caring is Sharing – Exploiting the Value in Data for Health and Innovation. (302): 701-705, 2023 [pdf]