Evidence map›Paper›PMID 35547826›Full record

ArticleFrontiers in artificial intelligence2022

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

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

Abstract read
In one paragraph

Article in Frontiers in artificial intelligence, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Mario CasilloInformation Communication Technologies (ICT) Center for Cultural Heritage, Department of Industrial Engineering (DIIN), University of Salerno, Fisciano, Italy.
Massimo De SantoInformation Communication Technologies (ICT) Center for Cultural Heritage, Department of Industrial Engineering (DIIN), University of Salerno, Fisciano, Italy.
Rosalba MoscaInformation Communication Technologies (ICT) Center for Cultural Heritage, Department of Industrial Engineering (DIIN), University of Salerno, Fisciano, Italy.
Domenico SantanielloInformation Communication Technologies (ICT) Center for Cultural Heritage, Department of Industrial Engineering (DIIN), University of Salerno, Fisciano, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

chatbotscultural heritageexperiential learningknowledge managementontology

Identifiers

PMID35547826
PMCPMC9083409

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LicenceCC BY
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.