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Mining tourist preferences and decision support via tourism-oriented knowledge graph

文献类型：期刊论文

作者Gao, Jialiang5,6; Peng, Peng5,6; Lu, Feng3,4,5,6; Claramunt, Christophe2,6; Qiu, Peiyuan1,6; Xu, Yang5,6
刊名INFORMATION PROCESSING & MANAGEMENT
出版日期2024
卷号61期号:1页码:24
ISSN号0306-4573
关键词Tourism knowledge graph Information extraction Spatiotemporal analysis Knowledge reasoning Decision-making support
DOI10.1016/j.ipm.2023.103523
通讯作者Peng, Peng(pengp@lreis.ac.cn)
英文摘要Currently, tourism management research is focused on comprehending the fluctuating tourist preferences and devising targeted development strategies through extensive analysis of heterogenous user-generated contents. However, given the online reviews of attractions involve overabundant mixed and intangible dimensions, the widely-used unsupervised text mining could be incomplete or inaccurate. Furthermore, the existing literature typically restricted to the certain types of attractions within several tourist destinations and origins, can hardly guarantee comprehensive insights. To overcome these limitations, the study proposes a novel knowledgegraph-driven framework, involving the systematic construction as well as the thorough investigation and inference of a tourism-oriented knowledge graph (TKG). Following the ontology of domain expertise, 11,296,716 structured triplets of multifaceted knowledge about 1,174,034 tourists and 20,481 attractions within all 340 city-level destinations across China are extracted from multi-source text corpus by the transferring learning on pre-training language model with 43.64-50.65 % accuracy enhancement. In virtue of TKG, a comprehensive decision-support system can be established, which bifurcates into two distinct modes of knowledge application: symbolic query and distributed reasoning. Through the implementation of multiple spatiotemporal analyses via SPARQL queries on TKG, the distribution regularities of tourist preference, causal interpretations, and their effects on destination development can be progressively detected. Refining the distributed representations of objects by injecting abundant contextual knowledge from TKG can significantly enhance the downstream inferential tasks, such as tourist demand prediction and attraction competitive intelligence.
WOS关键词USER-GENERATED CONTENT ; DESTINATION ; REVIEWS ; ATTRACTIONS ; HOSPITALITY
资助项目National Key Research and Development Program[2022YFB3904200] ; National Natural Science Foundation of China[42001391] ; Chinese Academy of Sciences President's International Fellowship Initiative[YPI002] ; Yongth Project of Innovation LREIS ; [2021VTA0002]
WOS研究方向Computer Science ; Information Science & Library Science
语种英语
出版者ELSEVIER SCI LTD
WOS记录号WOS:001098794400001
资助机构National Key Research and Development Program ; National Natural Science Foundation of China ; Chinese Academy of Sciences President's International Fellowship Initiative ; Yongth Project of Innovation LREIS
源URL[http://ir.igsnrr.ac.cn/handle/311030/200214]  
专题中国科学院地理科学与资源研究所
通讯作者Peng, Peng
作者单位1.Shandong Jianzhu Univ, Sch Surveying & Geoinformat, Jinan 250101, Peoples R China
2.Brest Naval, Naval Acad Res Inst, Brest, France
3.Jiangsu Ctr Collaborat Innovat Geog Informat Resou, Nanjing 210023, Peoples R China
4.Fuzhou Univ, Acad Digital China, Fuzhou 350002, Peoples R China
5.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
6.Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China
推荐引用方式
GB/T 7714		 Gao, Jialiang,Peng, Peng,Lu, Feng,et al. Mining tourist preferences and decision support via tourism-oriented knowledge graph[J]. INFORMATION PROCESSING & MANAGEMENT,2024,61(1):24.
APA Gao, Jialiang,Peng, Peng,Lu, Feng,Claramunt, Christophe,Qiu, Peiyuan,&Xu, Yang.(2024).Mining tourist preferences and decision support via tourism-oriented knowledge graph.INFORMATION PROCESSING & MANAGEMENT,61(1),24.
MLA Gao, Jialiang,et al."Mining tourist preferences and decision support via tourism-oriented knowledge graph".INFORMATION PROCESSING & MANAGEMENT 61.1(2024):24.

入库方式： OAI收割

来源：地理科学与资源研究所

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