BUILDING A KNOWLEDGE GRAPH FOR A FACULTY PROFILING SYSTEM

ABSTRACT

This project addresses the limitations of traditional faculty profiling systems by leveraging the power of knowledge graphs. It aims to design, build, and test a knowledge graph that effectively represents the complex relationships between faculty members, their qualifications, areas of expertise, and other relevant attributes. The knowledge graph is constructed using the Neo4j graph database and the Cypher query language, enabling efficient querying and analysis of faculty data. The project's methodology involves data collection, preprocessing, schema definition, entity extraction, graph structure generation, and property assignment. The resulting knowledge graph serves as a valuable asset for academic institutions, providing a centralised and structured repository of faculty information. By capturing the interconnectedness between faculty members and their diverse attributes, the knowledge graph enables advanced querying capabilities, facilitates collaboration, and supports informed decision-making in various academic contexts. The project's outcomes contribute to the growing body of research on knowledge graph applications in academia, highlighting their potential to revolutionise the way academic data is managed and utilised.