  In today's rapidly evolving landscape of Artificial Intelligence, large
language models (LLMs) have emerged as a vibrant research topic. LLMs find
applications in various fields and contribute significantly. Despite their
powerful language capabilities, similar to pre-trained language models (PLMs),
LLMs still face challenges in remembering events, incorporating new
information, and addressing domain-specific issues or hallucinations. To
overcome these limitations, researchers have proposed Retrieval-Augmented
Generation (RAG) techniques, some others have proposed the integration of LLMs
with Knowledge Graphs (KGs) to provide factual context, thereby improving
performance and delivering more accurate feedback to user queries.
  Education plays a crucial role in human development and progress. With the
technology transformation, traditional education is being replaced by digital
or blended education. Therefore, educational data in the digital environment is
increasing day by day. Data in higher education institutions are diverse,
comprising various sources such as unstructured/structured text, relational
databases, web/app-based API access, etc. Constructing a Knowledge Graph from
these cross-data sources is not a simple task. This article proposes a method
for automatically constructing a Knowledge Graph from multiple data sources and
discusses some initial applications (experimental trials) of KG in conjunction
with LLMs for question-answering tasks.
