Contri(e)ve: Context + Retrieve for Scholarly Question Answering
<|reference_start|>Contri(e)ve: Context + Retrieve for Scholarly Question Answering: 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.<|reference_end|>
@article{shivashankar2024contri(e)ve:,
    title={Contri(e)ve: Context + Retrieve for Scholarly Question Answering},
    author={Kanchan Shivashankar and Nadine Steinmetz},
    journal={arXiv preprint arXiv:2409.09010},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.09010},
    primaryClass={cs.IR cs.AI}
}