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Dr. rer. nat. Mohamed Ahmed Sherif
Senior Researchers
Speaking Hours
Tuesday, 11:00 - 13:00
Always send an email to schedule an appointment before coming to the office. Without an appointment, I may not have time to meet with you or may not be in the office, even during office hours.
Thesis Supervision
I supervised/supported several bachelor, master and doctoral theses (sorted by the year of submission).
2019
- Learning Arbitrary RDF Dataset Enrichment Graphs Pre- & Postcondition Broadcasting (Master thesis)
2021
- OntoConnect: Domain-Agnostic Ontology Alignment using Neural Networks (PhD thesis)
2022
- Reinforced Active Learning for Link Discovery (Msc thesis)
- Link Discovery over geo RDF data: Content similarity approach (Bachelor thesis)
2023
- Leveraging Social Media in Disaster Situations (PhD thesis)
2024
- Geospatial Link Discovery With Human In The Loop (PhD thesis)
Research Interest
I am mainly interested in:
- Data engineering, knowledge graph construction & ontology building
- Knowledge graph integration using machine learning & large language models
- Novel data integration approaches for supporting NLP tasks such as entity summarization & keyphrase extraction
- Large language model for supporting data integration tasks and vice versa
Reviewing
My limited free time is partly consumed by tasks such as reviewing, for instance,”
Projects
ClimatebOWL – Climate neutral Business in Ostwestfalen-LippeDIESEL – Distributed Search in Large Enterprise DataFAIROmics – FAlRification of multiOmics data to link databases and create knowledge graphs for fermented foodsGEISER – Von Sensordaten zu internetbasierten Geo-ServicesGeoKnow – Making the Web an Exploratory for Geospatial KnowledgeHOBBIT – Holistic Benchmarking of Big Linked DataIngrid – Informationssystem Graffiti in DeutschlandLinkingLOD – Interlinking knowledge basesSAGE – Semantic Geospatial AnalyticsSAKE – With RDF and Machine Learning Getting Results FasterSLIPO – Scalable Linking of Integration of Big POI DataWHALE – Web-Scale Hybrid Explainable Machine Learning