Construction of Tourism Attraction Knowledge Graph Based on Web Text and Transfer Learning

被引：0
|
作者：
Gao J. [1 ,2 ]
Lu F. [1 ,2 ,3 ,4 ]
Peng P. [1 ]
Xu Y. [1 ,2 ]
机构：
[1] State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing
[2] College of Resources and Environment, University of Chinese Academy of Sciences, Beijing
[3] Fujian Collaborative Innovation Center for Big Data Applications in Governments, Fuzhou
[4] Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing
来源：
Wuhan Daxue Xuebao (Xinxi Kexue Ban)/Geomatics and Information Science of Wuhan University | 2022年 / 47卷 / 08期
基金：
中国国家自然科学基金;
关键词：
knowledge graph; tourism management; transfer learning; web text mining;
D O I：
10.13203/j.whugis20220120
中图分类号：
学科分类号：
摘要：
Objectives The rapid development of information and communication technology has facilitated the online tourism service and massive web text, which provides a new opportunity for tourism sector planning and personalized recommendation. However, owing to the characteristics of semantic vagueness and low signal-to-noise ratio, the web text is difficult to get utilized directly. Therefore, how to integrate the technologies of knowledge engineering, natural language processing and machine learning, so as to form a formalized domain knowledge graph from abundant tourism text, has attracted much attention. Methods This paper proposes a tourism knowledge graph construction method based on tourism domain ontology and transfer learning. Firstly, the ontology of tourist attractions is defined based on the domain specifications and standards, which support a comprehensive and systematic description of the semantic characteristics of attractions. Secondly, a transfer learning method is adopted to transform the pre-training language model into a customized knowledge extractor to acquire knowledge triples accurately from web text, which is integrated with the scattered tourism-related information including tourist check-ins and POI (point of interest) attributes to build a systematic knowledge graph. Results Experimental results show that the proposed knowledge extractor improves the accuracy (average area under the curve) and integrity (the number of sematic characteristics) of acquisition of sematic knowledge by 50.7% and 670%, respectively, compared with the common LDA (latent Dirichlet allocation) model. The constructed knowledge graph of tourist attractions contained 77 039 entities, 16 types of relationship, and total 10 971 810 triples. Conclusions Through the unified organization paradigm of triplet knowledge, the study realizes the fusion and integration of multi-source heterogeneous tourism data, and addresses the potential systemic risk in the decision-making process based on a single data source. It is argued that the constructed knowledge graph can fully capture the real tourism scene, support in-depth analysis of tourist behaviors and demands at different scales and granularities, and provide decision support for sustainable developments of tourist destinations. © 2022 Wuhan University. All rights reserved.
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页码：1191 / 1200and1219
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