# Leveraging Large Language Models for Semantic Query Processing in a Scholarly Knowledge Graph

arXiv: https://arxiv.org/abs/2405.15374 · HTML: https://mlsanity.com/paper/2405.15374/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 32 references that are in the map.
- First posted on arXiv 24 May 2024.

## Builds on (most cited first, 10 of 10)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space](https://mlsanity.com/paper/1902.10197/) (2019)
- [Texygen: A Benchmarking Platform for Text Generation Models](https://mlsanity.com/paper/1802.01886/) (2018)
- [Learning from the Worst: Dynamically Generated Datasets to Improve Online Hate Detection](https://mlsanity.com/paper/2012.15761/) (2020)
- [Domain-specific Knowledge Graphs: A survey](https://mlsanity.com/paper/2011.00235/) (2020)
- [Attend, Copy, Parse -- End-to-end information extraction from documents](https://mlsanity.com/paper/1812.07248/) (2018)
- [Enriching BERT with Knowledge Graph Embeddings for Document Classification](https://mlsanity.com/paper/1909.08402/) (2019)
- [TNNT: The Named Entity Recognition Toolkit](https://mlsanity.com/paper/2108.13700/) (2021)

## Built on by (most cited first, 5 of 5)

- [Combining LLMs and Knowledge Graphs to Reduce Hallucinations in Question Answering](https://mlsanity.com/paper/2409.04181/) (2024)
- [Contri(e)ve: Context + Retrieve for Scholarly Question Answering](https://mlsanity.com/paper/2409.09010/) (2024)
- [Large Language Models and Knowledge Graphs for Astronomical Entity Disambiguation](https://mlsanity.com/paper/2406.11400/) (2024)
- [Performance Evaluation of LLMs in Automated RDF Knowledge Graph Generation](https://mlsanity.com/paper/2603.29878/) (2026)
- [Reading Between the Citations: A Typed Claim Network for Scientific Literature](https://mlsanity.com/paper/2605.30966/) (2026)

## Abstract (arXiv)

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.