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Essam was invited to deliver a talk on “A Data Discovery Platform Empowered by Knowledge Graph Technologies: Challenges and Opportunities” at the Data Science seminar (Tartu University).
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Hussein Abdallah, Duc Nguyen, Kien Nguyen, Essam Mansour: Demonstration of KGNet: a Cognitive Knowledge Graph Platform. International Semantic Web Conference (ISWC) 2021.
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Reham Omar, Ishika Dhall, Nadia Sheikh, Essam Mansour: A Knowledge Graph Question-Answering Platform Trained Independently of the Graph. International Semantic Web Conference (ISWC) 2021.
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Essam Mansour: A Data Discovery Platform Empowered by Knowledge GraphTechnologies: Challenges and Opportunities. SEA-Data@VLDB 2021: 46-47.
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Ahmed Helal, Mossad Helali, Khaled Ammar, Essam Mansour: A Demonstration of KGLac: A Data Discovery and Enrichment Platform for Data Science. Proc. VLDB Endow. 14(12): 2675-2678 (2021).
Principal investigator (PI)
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Alumni - Master Student
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We are hiring Postdocs and PhD Students!
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Towards Cognitive Data Science Platforms: Challenges and Opportunities
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Towards Cognitive Data Science Platforms: Challenges and Opportunities
KGLiDS is a knowledge graph-based platform for linked data science.
KGQAn aims to develop a data science chatbot that can answer questions from different knowledge graphs without prior knowledge of the graphs. We are developing KGQAn as a chat assistant that guides data scientists to easily explore data science projects’ findings.
KGNet aims to develop an embedding as a service (EaaS) that can extend different RDF engines to support various embedding techniques. EaaS is a step forward to extend RDF engines to support embedding-based operators to explore better KGs based on semantics and classification models.
KGpip is scalable AutoML approach based on a novel formulation for the AutoML problem as a graph generation problem. In KGpip, we train a novel meta-learning on top of of our knowledge graph for linked data science to pose learner and pre-processing selection as a generation of different graphs representing ML pipelines. For more information, please read our KGpip paper
AlphaBot is a weak supervision-based approach to improve chatbots for code repositories. We evaluate AlphaBot using a dataset that composes of 749 queries representing 52 intents. Our results show that AlphaBot helps chatbot practitioners to boost the NLU’s performance at early releases of their chatbots (i.e., fewer training queries). In particular, we find that our approach increases the NLU’s performance up to 44% compared to the baseline. Also, the results show that AlphaBot annotates, on average, 99% of queries correctly.
This project aims at developing a feature store for data science projects. Discovering features is one of the applications on top of our knowledge graph for linked data science.
This project aims at developing a platform for detecting advanced persistent threats (APT) based on knowledge graph technologies. Our approach utilizes graph neural network and semantic graph similarity to detect attack scenarios in a provenance graph of network logs.
This project aims at developing a deep active learning platform for triple extraction tasks from the English text. Our platform automates the dataset annotation process required for training models for question understanding or knowledge graph construction.
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Undergraduate course, University 1, Department, 2014
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Workshop, University 1, Department, 2015
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