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First, a framework of algorithms for insertion of new indicators and projection on the HSFC curve based on their transformer-based similarity assessment, for retrieval of indicators and load-balancing along with an approach for data classification of entrant-indicators is described. Then, a thorough case study in a distributed knowledge graph environment experimentally evaluates our framework. The results are presented and discussed in light of theory along with the actual impact that can have for practitioners analysing SDG data, including intergovernmental organizations, government agencies and social welfare organizations. Our approach empowers SDG knowledge graphs for causal analysis, inference, and manifold interpretations of the societal implications of SDG-related actions, as data are accessed in reduced retrieval times. 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引用次数: 0
摘要
可持续发展是指在不损害子孙后代资源的前提下提高当前生活水平。可持续发展目标(sdg)量化了可持续发展的成就,为子孙后代创造一个值得生活的世界铺平了道路。学者可以在分析可持续发展目标数据的基础上指导实践者的行动,从而为实现可持续发展目标做出贡献,这也是本工作的目的。我们提出了一种基于降维方法的算法框架,利用希尔伯特空间填充曲线(Hilbert Space Filling Curves, hsfc)对新的未分类的可持续发展目标数据和新的指标进行语义聚类,并有效地将它们放置在分布式知识图存储环境中。首先,描述了基于变压器相似性评估的新指标插入和HSFC曲线投影的算法框架,用于指标检索和负载平衡,以及进入指标的数据分类方法。然后,在分布式知识图环境中进行了全面的案例研究,实验评估了我们的框架。结果在理论的基础上提出和讨论,以及对分析可持续发展目标数据的实践者的实际影响,包括政府间组织、政府机构和社会福利组织。我们的方法使可持续发展目标知识图谱能够进行因果分析、推理,并对可持续发展目标相关行动的社会影响进行多种解释,因为数据可以在更短的检索时间内访问。它有助于更快地衡量用户和社区对特定目标的影响,并有助于更快地进行分布式知识匹配,因为数据的语义内聚得到了保留。
A Knowledge Graph-Based Deep Learning Framework for Efficient Content Similarity Search of Sustainable Development Goals Data
ABSTRACT Sustainable development denotes the enhancement of living standards in the present without compromising future generations’ resources. Sustainable Development Goals (SDGs) quantify the accomplishment of sustainable development and pave the way for a world worth living in for future generations. Scholars can contribute to the achievement of the SDGs by guiding the actions of practitioners based on the analysis of SDG data, as intended by this work. We propose a framework of algorithms based on dimensionality reduction methods with the use of Hilbert Space Filling Curves (HSFCs) in order to semantically cluster new uncategorised SDG data and novel indicators, and efficiently place them in the environment of a distributed knowledge graph store. First, a framework of algorithms for insertion of new indicators and projection on the HSFC curve based on their transformer-based similarity assessment, for retrieval of indicators and load-balancing along with an approach for data classification of entrant-indicators is described. Then, a thorough case study in a distributed knowledge graph environment experimentally evaluates our framework. The results are presented and discussed in light of theory along with the actual impact that can have for practitioners analysing SDG data, including intergovernmental organizations, government agencies and social welfare organizations. Our approach empowers SDG knowledge graphs for causal analysis, inference, and manifold interpretations of the societal implications of SDG-related actions, as data are accessed in reduced retrieval times. It facilitates quicker measurement of influence of users and communities on specific goals and serves for faster distributed knowledge matching, as semantic cohesion of data is preserved.