Deskripsi
Traditional recruitment method that rely on manual resume screening have proven in adequate to process high volume of applications efficiently and meet quality assessment standards. This industry- specific problem calls for an innovative technology solution that can revolutionize the candidate evalu- ation process. The limitation of conventional recruitment method have led us to explore semantic tech- nologies specially using an integrated system of Knowledge Graphs(KG) and Large Language Model (LLM). Our motivation comes from the need to transform unstructured resume data into structured, query-able knowledge that enables faster and more accurate candidate matching. Our innovative solutions combines the semantic relationship capabilities of KG 2 with the natural language understanding of LLMs 3, specially implementing Retrieval-Augmented Generation (RAG) to provide contextually relevant candidate recommendations 4
