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About CoDS

Posts

Invited Talk at Tartu University

less than 1 minute read

Published:

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).

KGNet demo at ISWC

less than 1 minute read

Published:

Hussein Abdallah, Duc Nguyen, Kien Nguyen, Essam Mansour: Demonstration of KGNet: a Cognitive Knowledge Graph Platform. International Semantic Web Conference (ISWC) 2021.

KGQAn demo at ISWC

less than 1 minute read

Published:

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.

Invited talk at SEA-Data@VLDB

less than 1 minute read

Published:

Essam Mansour: A Data Discovery Platform Empowered by Knowledge GraphTechnologies: Challenges and Opportunities. SEA-Data@VLDB 2021: 46-47.

KGLac demo at VLDB

less than 1 minute read

Published:

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).

members

news

Invited Talk at IVADO

less than 1 minute read

Published:

Towards Cognitive Data Science Platforms: Challenges and Opportunities

projects

KGQAn: A Data Science Chat Assistant Platform

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: KG Neural Network Query Engine

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: A Scalable AutoML Approach Based on GNN

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: Improving Chatbots for Code Repositories

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.

KGAPT: an APT Detection Approach based on GNN

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.

KG-DAL: Automatic Annotation for KG related tasks

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.

talks

teaching

Teaching experience 1

Undergraduate course, University 1, Department, 2014

This is a description of a teaching experience. You can use markdown like any other post.

Teaching experience 2

Workshop, University 1, Department, 2015

This is a description of a teaching experience. You can use markdown like any other post.