Rawaa Alatrash
Scholar

Rawaa Alatrash

Google Scholar ID: i8uXpZQAAAAJ
PhD Researcher at University of Duisburg-Essen
Intelligent Recommender SystemsDeep Machine LeaningKnowledge GraphsSentiment Analysis
Citations & Impact
All-time
Citations
382
 
H-index
11
 
i10-index
14
 
Publications
20
 
Co-authors
7
list available
Resume (English only)
Academic Achievements
  • - 2024:
  • * Transparent Learner Knowledge State Modeling using Personal Knowledge Graphs and Graph Neural Networks
  • * ConceptGCN: Concept Recommendation in MOOCs based on Knowledge Graph Convolutional Networks and SBERT
  • * Designing and Evaluating an Educational Recommender System with Different Levels of User Control
  • * Learner Modeling and Recommendation of Learning Resources using Personal Knowledge Graphs
  • - 2023:
  • * Automatic Construction of Educational Knowledge Graphs: A Word Embedding-based Approach
Research Experience
  • - Since May 2022: Ph.D. student in the Social Computing Group at the University of Duisburg-Essen, Germany
  • - March 2019 – September 2019: Website Developer at Wholesaller in Bhubaneswar, India
  • - January 2018 – September 2018: Website Developer at Delta Trading & Contracting in Lebanon
Education
  • - June 2018 – June 2020: Master of Technology, Computer Science and Engineering, C.V. Raman Global University, Bhubaneswar, Odisha, India
  • - March 2012 - January 2018: Bachelor of Information Engineering, Development of Application Programming, Syrian Virtual University, Damascus, Syria
Background
  • - Research Interests: Recommender Systems, Deep Learning and Machine Learning, Natural Language Processing (NLP), Knowledge Base Systems, Text Data Mining and Knowledge Discovery, Sentiment Analysis, Social Computing
  • - Technical Expertise: Cross-platform proficiency, advanced knowledge in programming languages
  • - Experience: Multiple years of experience in developing interactive websites using various web technologies; experience in various algorithms in both Machine Learning and Deep Learning to improve Natural Language Processing techniques