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一种基于本体的聊天机器人,用于增强文化遗产场景中的体验式学习。

An Ontology-Based Chatbot to Enhance Experiential Learning in a Cultural Heritage Scenario.

作者信息

Casillo Mario, De Santo Massimo, Mosca Rosalba, Santaniello Domenico

机构信息

Information Communication Technologies (ICT) Center for Cultural Heritage, Department of Industrial Engineering (DIIN), University of Salerno, Fisciano, Italy.

出版信息

Front Artif Intell. 2022 Apr 25;5:808281. doi: 10.3389/frai.2022.808281. eCollection 2022.

DOI:10.3389/frai.2022.808281
PMID:35547826
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9083409/
Abstract

Italy is rich in cultural attractions, many known worldwide, others more hidden and unrecognized. Cultural attractions include tangible cultural assets (works of art, archaeological excavations, and churches) and intangible ones (music, poetry, and art). Today, given the pervasive diffusion of "smart" devices, the intelligent use of modern technologies could play a crucial role in changing the habit of consulting and visiting cultural heritage mainly with traditional methodologies, making little or no use of the advantages coming from the more and more availability of digitalized resources. A realm of particular interest is "experiential learning" when applied to cultural heritage, where tourists more and more ask to be helped in discovering the richness of sites they explore. In this article, we will present an innovative chatbot-based system, called HeriBot, that supports experiential tourism. Our system has been developed and experimented with a research effort for applying ICT technologies to enhance the knowledge, valorization, and sustainable fruition of the Cultural Heritage related to the Archaeological Urban Park of Naples (PAUN-Parco Archeologico Urbano di Napoli). Our article starts exploiting the ontological approach based on a purpose ontology describing the Park Heritage. Using such an ontology, we designed a chatbot that can identify the specific characteristics and motivations of the tourist, defining language, tone, and visitable scenarios and, through the ontology, allows the visit to be transformed into a personalized educational opportunity. The system has been validated in terms of dialogue effectiveness and training efficiency by a panel of experts, and we present and discuss obtained results.

摘要

意大利拥有丰富的文化景点,其中许多闻名于世,另一些则更为隐匿且未被认知。文化景点包括有形文化资产(艺术作品、考古发掘和教堂)和无形文化资产(音乐、诗歌和艺术)。如今,鉴于“智能”设备的广泛普及,现代技术的明智运用在改变主要以传统方法咨询和参观文化遗产的习惯方面可能发挥关键作用,而传统方法很少或根本没有利用数字化资源日益丰富所带来的优势。一个特别有趣的领域是应用于文化遗产的“体验式学习”,在这个领域,游客越来越希望在探索景点的丰富内涵时得到帮助。在本文中,我们将介绍一种名为HeriBot的基于聊天机器人的创新系统,该系统支持体验式旅游。我们的系统是通过将信息通信技术应用于增强与那不勒斯考古城市公园(PAUN - 那不勒斯考古城市公园)相关的文化遗产的知识、价值提升和可持续利用的研究工作而开发和试验的。我们的文章首先利用基于描述公园遗产的目的本体的本体论方法。利用这样的本体,我们设计了一个聊天机器人,它可以识别游客的特定特征和动机,定义语言、语气和可参观场景,并通过本体将参观转变为个性化的教育机会。该系统已由专家小组在对话有效性和训练效率方面进行了验证,我们展示并讨论了所获得的结果。

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本文引用的文献

1
An intelligent Chatbot using deep learning with Bidirectional RNN and attention model.一个使用双向循环神经网络和注意力模型进行深度学习的智能聊天机器人。
Mater Today Proc. 2021;34:817-824. doi: 10.1016/j.matpr.2020.05.450. Epub 2020 Jun 10.
2
Analyze Informant-Based Questionnaire for The Early Diagnosis of Senile Dementia Using Deep Learning.基于深度学习的用于老年痴呆症早期诊断的信息提供者问卷分析
IEEE J Transl Eng Health Med. 2019 Dec 16;8:2200106. doi: 10.1109/JTEHM.2019.2959331. eCollection 2020.
3
A Novel Method to Detect Functional microRNA Regulatory Modules by Bicliques Merging.
一种通过双团合并检测功能性微小RNA调控模块的新方法。
IEEE/ACM Trans Comput Biol Bioinform. 2016 May-Jun;13(3):549-56. doi: 10.1109/TCBB.2015.2462370.
4
Deep learning in neural networks: an overview.神经网络中的深度学习:综述。
Neural Netw. 2015 Jan;61:85-117. doi: 10.1016/j.neunet.2014.09.003. Epub 2014 Oct 13.
5
The mice that warred.
Sci Am. 2001 Jun;284(6):34-5. doi: 10.1038/scientificamerican0601-34.