# Contri(e)ve: Context + Retrieve for Scholarly Question Answering

arXiv: https://arxiv.org/abs/2409.09010 · HTML: https://mlsanity.com/paper/2409.09010/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 12 references that are in the map.
- First posted on arXiv 13 Sep 2024.

## Builds on (most cited first, 9 of 9)

- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [DBLP-QuAD: A Question Answering Dataset over the DBLP Scholarly Knowledge Graph](https://mlsanity.com/paper/2303.13351/) (2023)
- [Leveraging LLMs in Scholarly Knowledge Graph Question Answering](https://mlsanity.com/paper/2311.09841/) (2023)
- [SemOpenAlex: The Scientific Landscape in 26 Billion RDF Triples](https://mlsanity.com/paper/2308.03671/) (2023)
- [Large Language Models for Scientific Information Extraction: An Empirical Study for Virology](https://mlsanity.com/paper/2401.10040/) (2024)
- [Question Answering on Scholarly Knowledge Graphs](https://mlsanity.com/paper/2006.01527/) (2020)
- [Open Research Knowledge Graph:A System Walkthrough](https://mlsanity.com/paper/2206.01439/) (2022)
- [Leveraging Large Language Models for Semantic Query Processing in a Scholarly Knowledge Graph](https://mlsanity.com/paper/2405.15374/) (2024)
- [Evaluating author name disambiguation for digital libraries: A case of DBLP](https://mlsanity.com/paper/1806.10540/) (2018)

## Built on by (most cited first, 2 of 2)

- [Hybrid-SQuAD: Hybrid Scholarly Question Answering Dataset](https://mlsanity.com/paper/2412.02788/) (2024)
- [WikiSTAR: A System for Shedding Light on the Hidden History of Scientific Wikipedia Articles](https://mlsanity.com/paper/2607.12441/) (2026)

## Abstract (arXiv)

Scholarly communication is a rapid growing field containing a wealth of knowledge. However, due to its unstructured and document format, it is challenging to extract useful information from them through conventional document retrieval methods. Scholarly knowledge graphs solve this problem, by representing the documents in a semantic network, providing, hidden insights, summaries and ease of accessibility through queries. Naturally, question answering for scholarly graphs expands the accessibility to a wider audience. But some of the knowledge in this domain is still presented as unstructured text, thus requiring a hybrid solution for question answering systems. In this paper, we present a two step solution using open source Large Language Model(LLM): Llama3.1 for Scholarly-QALD dataset. Firstly, we extract the context pertaining to the question from different structured and unstructured data sources: DBLP, SemOpenAlex knowledge graphs and Wikipedia text. Secondly, we implement prompt engineering to improve the information retrieval performance of the LLM. Our approach achieved an F1 score of 40% and also observed some anomalous responses from the LLM, that are discussed in the final part of the paper.