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 within
academic papers, while KGQP leverages the KG structure to improve query
accuracy and efficiency with LLMs.
By combining the ASKG with LLMs, our approach enhances knowledge utilization
and natural language understanding capabilities. The proposed system employs an
automatic LLM-SPARQL fusion to retrieve relevant facts and textual nodes from
the ASKG. Initial experiments demonstrate that our framework is superior to
baseline methods in terms of accuracy retrieval and query efficiency.
We showcase the practical application of our framework in academic research
scenarios, highlighting its potential to revolutionize scholarly knowledge
management and discovery. This work empowers researchers to acquire and utilize
knowledge from documents more effectively and provides a foundation for
developing precise and reliable interactions with LLMs.

本研究旨在开发一种创新的语义查询处理系统，允许用户获取澳大利亚国立大学计算机科学（CS）研究员们所产生的研究作品的全面信息。该系统将大型语言模型（LLMs）与澳大利亚国立大学 CS 领域的 ANU Scholarly Knowledge Graph（ASKG）相结合，ASKG 是存储 ANU 所有研究相关产物的结构化知识库。通过结合 Deep Document Model（DDM）进行全面文档表示和 KG-enhanced Query Processing（KGQP）进行优化复杂查询处理的知识图融合方法，以解决传统学术知识图建设和利用方法的局限性，该方法能够获取来自 ASKG 的相关事实和文本节点，并在实验中显示出优于基准方法的结果，具有提高查询准确性和效率的潜力，为开发与 LLMs 的精确可靠交互奠定基础，强化了知识利用与自然语言理解能力。这项工作展示了该框架在学术研究场景中的实际应用，凸显其革新学术知识管理与发现的潜力，使研究人员更有效地获取和利用文档中的知识。