Semantic Search Using LLM-Aided Topic Generation on Knowledge Graphs for Paper Discovery

The exponential growth of academic papers presents a huge challenge for researchers, exacerbating the already tedious literature review process. Current tools like Google Scholar and Connected Papers offer solutions for text-based and citation-based searches but fail to address the need for finding semantically similar yet terminologically different papers efficiently. This paper proposes an innovative approach to paper discovery using semantic search to create a knowledge graph of topics and papers. By generating a tree of topics using ChatGPT 4o and calculating semantic similarity with SciBERT, this method aims to uncover relevant papers overlooked by traditional citation-based searches. The solution, validated through quantitative evaluation, demonstrates the potential to improve the efficiency and comprehensiveness of paper discovery.