Naeima Hamed

(she/her)

BSc (Hons), MSc, PhD

Publication

• Date
• Type
• Promoted

Research

 My research addresses real-world challenges by creating ontologies, building knowledge graphs, and applying automated reasoning to achieve practical solutions.

• The Forest Observatory Ontology (FOO): One of my key projects is the Forest Observatory Ontology (FOO) (ontology.forest-observatory.cardiff.ac.uk), developed with input from domain experts and wildlife data provided by Danau Girang Field Centre - Cardiff University.   FOO brings together diverse wildlife datasets into an ontology-based knowledge graph. This knowledge graph was used in training deep learning models and enabling semantic reasoning. Using historical GPS sensor data collected from collars fitted around elephants’ necks,I trained a deep learning model using Google's TensorFlow and Keras framework to predict their movements with 99.04% accuracy, outperforming traditional methods such as linear regression (90.95%) and vector autoregression (91.64%). Semantic reasoning rules were also applied to predict potential poaching incidents.

• The Internet of Things (IoT) datamarket places: I generalised my semantic data integration approach to a different domain, specifically IoT data marketplaces. This approach allowed data consumers, instead of purchasing sensor datasets in bulk, to buy only the specific data needed for tasks such as training AI models. For this project, I developed an ontology with input from domain experts and populated it with data from six heterogeneous sensors, where each sensor maintained its own knowledge graph. Semantic reasoning rules were applied to these knowledge graphs to address practical use cases. The semantic data integration approach was evaluated using three configurations, where portions of each sensor’s knowledge graph were stored on resource-constrained edge devices, and federated SPARQL queries were executed to retrieve data from these devices. The experiments measured accuracy and response times across the configurations. The findings demonstrated that decentralised knowledge graphs, stored on edge devices with embedded reasoning rules, responded more efficiently to federated SPARQL queries. This configuration was recommended as the optimal setup for future IoT data marketplace deployments. 

To explore my research more in depth please visit my publications tab.

Teaching

• Supervising undergraduate group projects (CM2305: Group Project).
•  Graduate tutor for (CM2203: Informatics).

Biography

Education and Qualifications
2024: PhD (Computer Science and Informatics), Cardiff University
2019: MSc (Data Science and Analytics, Distinction), Cardiff University
2000: BSc (Computer Engineering), The Future University, Khartoum, Sudan

Career Overview
2020 - 2024  Doctoral Researcher, Cardiff University School of Computer Science and Informatics
2018 - 2019: MSc Student, Cardiff University

Research Focus and Contributions

• Addressing data silos in forest observatories through semantic web technologies and AI.
• Developing frameworks for data sharing, interoperability, and analysis using ontologies and knowledge graphs.
• Generalising methodologies for broader IoT applications and publishing findings in leading journals and conferences.

Honours and awards

• Best Research Presentation 2023: 1st Prize (Year 3) - January PGR Workshop (Presentations and Posters), Awarded by Cardiff University based on PGR Student Scoring.

Professional memberships