RoboProf-CU

The project documented in the report involved creating an intelligent agent, specifically a knowledge base and a chatbot for Concordia University’s course catalog. The primary objective was to enhance the representation and accessibility of academic data using semantic web technologies.

Summary of the Project:

Knowledge Base Construction:

• Data from Concordia’s course catalog was utilized to populate a knowledge base using RDF (Resource Description Framework).
• Technologies like RDFLib, Pandas, and various namespaces (FOAF, RDFS) were employed to structure the data.
• A schema was designed incorporating both common and custom vocabularies to comprehensively represent academic entities and relationships.

Knowledge Base Population:

• Document processing and entity recognition were performed using Python libraries such as SpaCy and Apache Tika to extract and link data.
• The knowledge base was populated with RDF triples, mapping courses, students, and educational content through a structured graph.

RoboProf Chatbot:

• A chatbot was developed using the Rasa framework, capable of answering queries about the course catalog by interacting with the knowledge base via SPARQL endpoints.
• The chatbot supports queries about courses, topics covered, and specific course details.

Technologies Used:

• RDF, RDFLib for semantic web modeling
• Rasa for building the chatbot
• Pandas for data manipulation
• Python, SpaCy for natural language processing and entity recognition
• Apache Tika for document conversion
• SPARQL for querying the RDF data

This project successfully demonstrates the application of semantic web technologies and natural language processing to enhance the accessibility and interactivity of educational data systems.

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