[2405.15374] Leveraging Large Language Models for Semantic Query Processing in a Scholarly Knowledge Graph

Computer Science > Information Retrieval arXiv:2405.15374 (cs) [Submitted on 24 May 2024 (v1), last revised 4 Mar 2026 (this version, v2)] Title:Leveraging Large Language Models for Semantic Query Processing in a Scholarly Knowledge Graph Authors:Runsong Jia, Bowen Zhang, Sergio J. Rodríguez Méndez, Pouya G. Omran View a PDF of the paper titled Leveraging Large Language Models for Semantic Query Processing in a Scholarly Knowledge Graph, by Runsong Jia and 3 other authors View PDF HTML (experimental) Abstract:The proposed research aims to develop an innovative semantic query processing system that enables users to obtain comprehensive information about research works produced by Computer Science (CS) researchers at the Australian National University (ANU). The system integrates Large Language Models (LLMs) with the ANU Scholarly Knowledge Graph (ASKG), a structured repository of all research-related artifacts produced at ANU in the CS field. Each artifact and its parts are represented as textual nodes stored in a Knowledge Graph (KG). To address the limitations of traditional scholarly KG construction and utilization methods, which often fail to capture fine-grained details, we propose a novel framework that integrates the Deep Document Model (DDM) for comprehensive document representation and the KG-enhanced Query Processing (KGQP) for optimized complex query handling. DDM enables a fine-grained representation of the hierarchical structure and semantic relationships withi...