John Akinyemi

Assistant Professor
School of Computer Science
Email: 
jakinyem@uoguelph.ca
Office: 
REYN 2022

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Graph Databases, Information retrieval, Knowledge Graphs, Semantic Information Retrieval, Semantic Information Storage, Software Engineering

Education and Employment Background

Dr. John Akinyemi earned his PhD in Computer Science from the University of Waterloo in 2012. He completed an MSc in Computer Science at the University of Manitoba in 2006 and obtained a Bachelor of Technology from the Federal University of Technology, Akure, in 1997.


Research Themes: Advancing Semantic Information Retrieval Through Graph-Based Models

This research investigates the integration of Information Retrieval, Graph Databases, Knowledge Graphs, and Software Engineering to enhance semantic information storage, retrieval, and utilization. By leveraging graph-based models, it improves the contextual understanding of complex data relationships, enabling more accurate, efficient, and userfriendly information retrieval systems while addressing challenges in knowledge discovery and intelligent systems design.

  1. Domain-Specific Language Modelling: This research investigates the development of domain-specific language models, leveraging knowledge graphs to train models on datasets encompassing domain ontologies and software artifacts.
  2. Semantic Graph Models for Intelligent Information Retrieval: Focuses on developing graph-based representations of semantic relationships in structured, unstructured, and semi-structured data. It incorporates domain ontologies and graph databases to enable advanced query capabilities. These innovations enhance search engines and recommender systems.
  3. Knowledge Graphs for Repositories: Applying Knowledge Graphs to enterprise data and repositories like GitHub uncovers hidden insights into developer activities, code dependencies, and bug-tracking. This enhances collaboration and project management, leading to more productive software development teams.
  4. AI-Powered Semantic Information Storage: Combining AI with semantic storage in graph databases optimizes information organization and retrieval, transforming industries like finance, e-commerce, and public services through more personalized and accessible systems.
  5. Sustainability and Open-Source Collaboration: Emphasizing open standards and sustainable practices in semantic graph models fosters long-term innovation, positioning Canadian researchers and industries as global leaders in semantic information technologies.