Intelligent Query and Learning System for Logistics

This research project aims to create an Intelligent Query and Learning System (IQLS) that can integrate knowledge from multiple sources such as documents, web pages, voices, input texts, images, and videos. The IQLS will use learning and reasoning techniques to update the knowledge structures dynamically and allow multiple users to integrate their knowledge packages and train the system with different views to answer questions. The proposed system will provide a platform for users to create and integrate their own knowledge, which will be useful in supporting logistics knowledge management for logistic chains and services. The expected outcome of this project is to develop a novel adaptive knowledge structure with dynamic update based on multiple sources that can provide an efficient way to manage and query diverse data holdings collected in the Canadian Logistics Data Vault regarding logistics and transportation networks. This will be useful in emergency procedures and data retrieval for transportation networks, including accidents, extreme weather events, and CBRN defense.

Faculty Supervisor:

Hossam Gaber

Student:

Partner:

Rheinisch-Westfälische Technische Hochschule Aachen

Discipline:

Computer science

Sector:

Education

University:

University of Ontario Institute of Technology

Program: