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Article
Publication date: 29 May 2023

Xiang Zheng, Mingjie Li, Ze Wan and Yan Zhang

This study aims to extract knowledge of ancient Chinese scientific and technological documents bibliographic summaries (STDBS) and provide the knowledge graph (KG) comprehensively…

Abstract

Purpose

This study aims to extract knowledge of ancient Chinese scientific and technological documents bibliographic summaries (STDBS) and provide the knowledge graph (KG) comprehensively and systematically. By presenting the relationship among content, discipline, and author, this study focuses on providing services for knowledge discovery of ancient Chinese scientific and technological documents.

Design/methodology/approach

This study compiles ancient Chinese STDBS and designs a knowledge mining and graph visualization framework. The authors define the summaries' entities, attributes, and relationships for knowledge representation, use deep learning techniques such as BERT-BiLSTM-CRF models and rules for knowledge extraction, unify the representation of entities for knowledge fusion, and use Neo4j and other visualization techniques for KG construction and application. This study presents the generation, distribution, and evolution of ancient Chinese agricultural scientific and technological knowledge in visualization graphs.

Findings

The knowledge mining and graph visualization framework is feasible and effective. The BERT-BiLSTM-CRF model has domain adaptability and accuracy. The knowledge generation of ancient Chinese agricultural scientific and technological documents has distinctive time features. The knowledge distribution is uneven and concentrated, mainly concentrated on C1-Planting and cultivation, C2-Silkworm, and C3-Mulberry and water conservancy. The knowledge evolution is apparent, and differentiation and integration coexist.

Originality/value

This study is the first to visually present the knowledge connotation and association of ancient Chinese STDBS. It solves the problems of the lack of in-depth knowledge mining and connotation visualization of ancient Chinese STDBS.

Details

Library Hi Tech, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0737-8831

Keywords

Open Access
Article
Publication date: 15 February 2022

Kyrie Eleison Munoz

This paper determines how travel intentions can be predicted using self-disclosure behaviour, trust and intimacy. This case study focuses on Tinder users who utilised the…

2114

Abstract

Purpose

This paper determines how travel intentions can be predicted using self-disclosure behaviour, trust and intimacy. This case study focuses on Tinder users who utilised the application's Passport feature which allowed them to travel virtually and interact with other users around the globe amid global travel restrictions.

Design/methodology/approach

This quantitative research conveniently sampled 294 Tinder users who used the Passport feature during COVID-19 pandemic lockdowns. Data were analysed using PLS-SEM.

Findings

This study revealed that self-disclosure had a significant influence towards future travel intentions. Findings show that the more users self-disclose, the more their intent to travel increase. Trust and intimacy also had significant relationship on travel intentions while intimacy had a mediating effect between self-disclosure and travel intentions.

Practical implications

Tourism-oriented establishments and destination marketers should consider Tinder users as a market segment of future tourists. These users have developed travel intentions through in-app interactions and thus comprise an untapped market of potential tourists seeking for meet-ups and niche experiences in a post-pandemic era.

Originality/value

This study provides novelty in showing the predictive relationship of self-disclosure, trust and intimacy towards travel intentions. A model consisting of these constructs in the context of online interactions was also empirically tested and found adequate to predict travel intentions.

Details

Journal of Tourism Futures, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2055-5911

Keywords

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