A novel rapid web investigation method for ecological agriculture patterns in China

Wang, Shu; Zhu, Yunqiang; Liu, Qian; Song, Jia L.; Yuan, Wen; Sun, Kai; Cheng, Quanying

doi:10.1016/j.scitotenv.2022.156653

Cited by 10 publications

(9 citation statements)

References 23 publications

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“…There are a plethora of innovative examples for Geo-KGs, including the spatial scene KG (Shen et al, 2022), geographic evolutionary KG (Zheng et al, 2022), disaster prediction KG (Ge et al, 2022), urban KG (Liu et al, 2023), smart transportation KG (Del Mondo et al, 2021;Qi et al, 2023), and the largest cross-domain KnowWhereGraph to date (Janowicz et al, 2022). The construction process of Geo-KG generally employs the multisource geospatial data fusion and knowledge extraction technology, encompassing geo-semantic parsing (Nizzoli et al, 2020), geo-entity identification, linkage, alignment (Liang et al, 2023;Sun, Hu, et al, 2021), topological relation acquisition (Wang, Zhu, et al, 2022), and expertise association. Based on the constructed Geo-KGs, there are two application modes tailored to a diverse array of downstream tasks.…”

Section: Geographic Knowledge Graphmentioning

confidence: 99%

Knowledge‐driven spatial competitive intelligence for tourism

Gao,

Peng,

et al. 2024

Transactions in GIS

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Competition among tourism enterprises is an ineluctable component of sustainable tourism growth, requiring comprehensive studies to understand its dynamic and develop appropriate strategies. The literature employs text mining or statistical analyses to identify correlations between tourism areas as competitive relationships. However, this approach may not be fully applicable, due to the sparsity of crucial coexistence phenomena, and may fail to investigate fine‐grained attractions' competition inside destination using large‐scale geospatial data. To overcome the limitations, this study proposes a knowledge‐driven competitive intelligence framework for tourism management, utilizing knowledge graph (KG) construction and inference technologies. First, multi‐mode heterogeneous tourism data are integrated into a unified KG, including tourist check‐in, online text, and basic geographic information. Second, the spatial‐dependent GNN‐based model absorbing abundant spatial semantic knowledge from tourism‐oriented KG can enhance the performance of competition reasoning. Third, with multiple analyses via symbolic queries on KG, a comprehensive panorama of competition situations can be revealed.

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Section: Geographic Knowledge Graphmentioning

confidence: 99%

Knowledge‐driven spatial competitive intelligence for tourism

Gao,

Peng,

et al. 2024

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“…In the IQA system, especially in the geographical field, the question handling is rather crude, and in most cases only the general semantic analysis to related questions occurs [20][21][22]. This paper reviewed the existing IQA_QC frameworks and found that these classifications were not sufficient to guide the IQA system in the aspects of semantic parsing and question querying.…”

Section: Motivationsmentioning

confidence: 99%

Question Classification for Intelligent Question Answering: A Comprehensive Survey

Sun,

Wang,

Zhu

et al. 2023

IJGI

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In the era of GeoAI, Geospatial Intelligent Question Answering (GeoIQA) represents the ultimate pursuit for everyone. Even generative AI systems like ChatGPT-4 struggle to handle complex GeoIQA. GeoIQA is domain complex IQA, which aims at understanding and answering questions accurately. The core of IQA is the Question Classification (QC), which mainly contains four types: content-based, template-based, calculation-based and method-based classification. These IQA_QC frameworks, however, struggle to be compatible and integrate with each other, which may be the bottleneck restricting the substantial improvement of IQA performance. To address this problem, this paper reviewed recent advances on IQA with the focus on solving question classification and proposed a comprehensive IQA_QC framework for understanding user query intention more accurately. By introducing the basic idea of the IQA mechanism, a three-level question classification framework consisting of essence, form and implementation is put forward which could cover the complexity and diversity of geographical questions. In addition, the proposed IQA_QC framework revealed that there are still significant deficiencies in the IQA evaluation metrics in the aspect of broader dimensions, which led to low answer performance, functional performance and systematic performance. Through the comparisons, we find that the proposed IQA_QC framework can fully integrate and surpass the existing classification. Although our proposed classification can be further expanded and improved, we firmly believe that this comprehensive IQA_QC framework can effectively help researchers in both semantic parsing and question querying processes. Furthermore, the IQA_QC framework can also provide a systematic question-and-answer pair/library categorization system for AIGCs, such as GPT-4. In conclusion, whether it is explicit GeoAI or implicit GeoAI, the IQA_QC can play a pioneering role in providing question-and-answer types in the future.

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“…The regional suitability evaluation of straw storage and transportation technology model has the commonness of general comprehensive evaluation and its own evaluation characteristics. The research team collected the evaluation indexes of agricultural sustainable development (Wang et al 2003;Qiu et al 2005), ecological cycle (Wang and Li 1990;Ma et al 2005), modern agricultural (Zhao 2008), and agricultural waste resources (Song et al 2011), and selected some indexes that have been used frequently in recent years. Then, a hierarchical index database was established as the reference sample of the regional suitability evaluation index system of straw storage and transportation technology model.…”

Section: Indicator Screeningmentioning

confidence: 99%

Preliminary construction of evaluation index system and method of regional suitability of straw storage and transportation mode

Zhang

Yao

et al. 2021

BioRes

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The industrial utilization of straw from the field is an important way to achieve its comprehensive utilization. Construction of an efficient collection system is the premise and foundation for the efficient utilization of straw resources. It has become an urgent problem to reduce the cost of storage and transportation of straw from the field. In order to construct an indexing system and method of regional suitability evaluation of straw storage and transportation technology model, a model based on regional suitability criteria for straw storage and transportation technology has been constructed by using literature analysis. The evaluation method of the straw storage and transportation technology model is briefly discussed. This method provides a quantifiable basis for screening, evaluation, integration, and popularization of the straw storage and transportation technology model.

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A novel rapid web investigation method for ecological agriculture patterns in China

Cited by 10 publications

References 23 publications

Knowledge‐driven spatial competitive intelligence for tourism

Knowledge‐driven spatial competitive intelligence for tourism

Question Classification for Intelligent Question Answering: A Comprehensive Survey

Preliminary construction of evaluation index system and method of regional suitability of straw storage and transportation mode

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