A Structure and Content Prompt-based Method for Knowledge Graph Question Answering over Scholarly Data
Abstract: Transformative artificially intelligent tools, such as ChatGPT, designed to generate sophisticated text indistinguishable from that produced by a human, are applicable across a wide range of contexts. The technology presents opportunities as well as, often ethical and legal, challenges, and has the potential for both positive and negative impacts for organisations, society, and individuals. Offering multi-disciplinary insight into some of these, this article brings together 43 contributions from experts in fields such as computer science, marketing, information systems, education, policy, hospitality and tourism, management, publishing, and nursing. The contributors acknowledge ChatGPT’s capabilities to enhance productivity and suggest that it is likely to offer significant gains in the banking, hospitality and tourism, and information technology industries, and enhance business activities, such as management and marketing. Nevertheless, they also consider its limitations, disruptions to practices, threats to privacy and security, and consequences of biases, misuse, and misinformation. However, opinion is split on whether ChatGPT’s use should be restricted or legislated. Drawing on these contributions, the article identifies questions requiring further research across three thematic areas: knowledge, transparency, and ethics; digital transformation of organisations and societies; and teaching, learning, and scholarly research. The avenues for further research include: identifying skills, resources, and capabilities needed to handle generative AI; examining biases of generative AI attributable to training datasets and processes; exploring business and societal contexts best suited for generative AI implementation; determining optimal combinations of human and generative AI for various tasks; identifying ways to assess accuracy of text produced by generative AI; and uncovering the ethical and legal issues in using generative AI across different contexts.
Show BibTeX
@inproceedings{DBLP:conf/semweb/00010U23,
author = {Longquan Jiang and
Xi Yan and
Ricardo Usbeck},
editor = {Debayan Banerjee and
Ricardo Usbeck and
Nandana Mihindukulasooriya and
Gunjan Singh and
Raghava Mutharaju and
Pavan Kapanipathi},
title = {A Structure and Content Prompt-based Method for Knowledge Graph Question
Answering over Scholarly Data},
booktitle = {Joint Proceedings of Scholarly {QALD} 2023 and SemREC 2023 co-located
with 22nd International Semantic Web Conference {ISWC} 2023, Athens,
Greece, November 6-10, 2023},
series = {{CEUR} Workshop Proceedings},
volume = {3592},
publisher = {CEUR-WS.org},
year = {2023},
url = {https://ceur-ws.org/Vol-3592/paper3.pdf},
timestamp = {Tue, 02 Jan 2024 17:44:44 +0100},
biburl = {https://dblp.org/rec/conf/semweb/00010U23.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}