知识图谱是一个跨学科研究主题,对推动语义理解、智能推理与大模型发展具有关键作用。文章从Web of Science核心合集中检索获取2010—2024年间1703篇知识图谱领域的研究论文,并通过h指数确定了78篇高被引文献。然后,使用VOSviewer构建...知识图谱是一个跨学科研究主题,对推动语义理解、智能推理与大模型发展具有关键作用。文章从Web of Science核心合集中检索获取2010—2024年间1703篇知识图谱领域的研究论文,并通过h指数确定了78篇高被引文献。然后,使用VOSviewer构建共被引网络并进行聚类分析,最终结合内容分析方法对识别出的50篇核心文献进行主题归纳与深入解读。研究发现,文献共被引方法能有效识别领域内的核心文献集群。研究选择的知识图谱领域的50篇核心文献,可归纳为四个主要研究方向:知识图谱理论基础与构建方法、知识图谱嵌入、基于知识图谱的知识推理以及基于知识图谱的推荐系统。展开更多
Barnyard millet(Echinochloa spp.) is one of the most underresearched crops with respect to characterization of genetic resources and genetic enhancement. A total of 95 germplasm lines representing global collection we...Barnyard millet(Echinochloa spp.) is one of the most underresearched crops with respect to characterization of genetic resources and genetic enhancement. A total of 95 germplasm lines representing global collection were evaluated in two rainy seasons at Almora,Uttarakhand, India for qualitative and quantitative traits and the data were subjected to multivariate analysis. High variation was observed for days to maturity, five-ear grain weight, and yield components. The first three principal component axes explained 73% of the total multivariate variation. Three major groups were detected by projection of the accessions on the first two principal components. The separation of accessions was based mainly on trait morphology. Almost all Indian and origin-unknown accessions grouped together to form an Echinochloa frumentacea group. Japanese accessions grouped together except for a few outliers to form an Echinochloa esculenta group. The third group contained accessions from Russia, Japan, Cameroon, and Egypt. They formed a separate group on the scatterplot and represented accessions with lower values for all traits except basal tiller number. The interrelationships between the traits indicated that accessions with tall plants, long and broad leaves, longer inflorescences, and greater numbers of racemes should be given priority as donors or parents in varietal development initiatives. Cluster analysis identified two main clusters based on agro-morphological characters.展开更多
[Objective] This paper aimed to construct the core collection of cassava germplasm. [Methods] Parameter values of six traits of 161 clones were ana-lyzed and evaluated. [ Results] Minkowski genetic distance w...[Objective] This paper aimed to construct the core collection of cassava germplasm. [Methods] Parameter values of six traits of 161 clones were ana-lyzed and evaluated. [ Results] Minkowski genetic distance was established for cluster analysis of core collection. Preferred sampling method （D 2C5S3） was suit-able for the construction of the core collection of cassava. A total of 25 core collections were obtained, which accounted for 15% of the original germplasm and could represent the genetic diversity and integrity of the original germplasms. Meanwhile, it reflected that the genetic background of the tested cassava germplasm was nar-row and the range of genetic diversity was quite small with genetic redundancy. [ Conclusions] Constructing core collection provided a theoretical foundation for the protection and utilization of cassava germplasms.展开更多
The density based notion for clustering approach is used widely due to its easy implementation and ability to detect arbitrary shaped clusters in the presence of noisy data points without requiring prior knowledge of ...The density based notion for clustering approach is used widely due to its easy implementation and ability to detect arbitrary shaped clusters in the presence of noisy data points without requiring prior knowledge of the number of clusters to be identified. Density-based spatial clustering of applications with noise （DBSCAN） is the first algorithm proposed in the literature that uses density based notion for cluster detection. Since most of the real data set, today contains feature space of adjacent nested clusters, clearly DBSCAN is not suitable to detect variable adjacent density clusters due to the use of global density parameter neighborhood radius Y,.ad and minimum number of points in neighborhood Np~,. So the efficiency of DBSCAN depends on these initial parameter settings, for DBSCAN to work properly, the neighborhood radius must be less than the distance between two clusters otherwise algorithm merges two clusters and detects them as a single cluster. Through this paper： 1） We have proposed improved version of DBSCAN algorithm to detect clusters of varying density adjacent clusters by using the concept of neighborhood difference and using the notion of density based approach without introducing much additional computational complexity to original DBSCAN algorithm. 2） We validated our experimental results using one of our authors recently proposed space density indexing （SDI） internal cluster measure to demonstrate the quality of proposed clustering method. Also our experimental results suggested that proposed method is effective in detecting variable density adjacent nested clusters.展开更多
基金Supported by National Natural Science Foundation of China(60675039)National High Technology Research and Development Program of China(863 Program)(2006AA04Z217)Hundred Talents Program of Chinese Academy of Sciences
文摘知识图谱是一个跨学科研究主题,对推动语义理解、智能推理与大模型发展具有关键作用。文章从Web of Science核心合集中检索获取2010—2024年间1703篇知识图谱领域的研究论文,并通过h指数确定了78篇高被引文献。然后,使用VOSviewer构建共被引网络并进行聚类分析,最终结合内容分析方法对识别出的50篇核心文献进行主题归纳与深入解读。研究发现,文献共被引方法能有效识别领域内的核心文献集群。研究选择的知识图谱领域的50篇核心文献,可归纳为四个主要研究方向:知识图谱理论基础与构建方法、知识图谱嵌入、基于知识图谱的知识推理以及基于知识图谱的推荐系统。
文摘Barnyard millet(Echinochloa spp.) is one of the most underresearched crops with respect to characterization of genetic resources and genetic enhancement. A total of 95 germplasm lines representing global collection were evaluated in two rainy seasons at Almora,Uttarakhand, India for qualitative and quantitative traits and the data were subjected to multivariate analysis. High variation was observed for days to maturity, five-ear grain weight, and yield components. The first three principal component axes explained 73% of the total multivariate variation. Three major groups were detected by projection of the accessions on the first two principal components. The separation of accessions was based mainly on trait morphology. Almost all Indian and origin-unknown accessions grouped together to form an Echinochloa frumentacea group. Japanese accessions grouped together except for a few outliers to form an Echinochloa esculenta group. The third group contained accessions from Russia, Japan, Cameroon, and Egypt. They formed a separate group on the scatterplot and represented accessions with lower values for all traits except basal tiller number. The interrelationships between the traits indicated that accessions with tall plants, long and broad leaves, longer inflorescences, and greater numbers of racemes should be given priority as donors or parents in varietal development initiatives. Cluster analysis identified two main clusters based on agro-morphological characters.
基金Supported by Guangxi Technological Development Project(14123006-33)National Industrial Technology System of Cassava(CARS-15-gxtyn)Special Germplasm Resources Protection by Ministry of Agriculture in South Asia(15RZZY-33)
文摘[Objective] This paper aimed to construct the core collection of cassava germplasm. [Methods] Parameter values of six traits of 161 clones were ana-lyzed and evaluated. [ Results] Minkowski genetic distance was established for cluster analysis of core collection. Preferred sampling method （D 2C5S3） was suit-able for the construction of the core collection of cassava. A total of 25 core collections were obtained, which accounted for 15% of the original germplasm and could represent the genetic diversity and integrity of the original germplasms. Meanwhile, it reflected that the genetic background of the tested cassava germplasm was nar-row and the range of genetic diversity was quite small with genetic redundancy. [ Conclusions] Constructing core collection provided a theoretical foundation for the protection and utilization of cassava germplasms.
文摘The density based notion for clustering approach is used widely due to its easy implementation and ability to detect arbitrary shaped clusters in the presence of noisy data points without requiring prior knowledge of the number of clusters to be identified. Density-based spatial clustering of applications with noise （DBSCAN） is the first algorithm proposed in the literature that uses density based notion for cluster detection. Since most of the real data set, today contains feature space of adjacent nested clusters, clearly DBSCAN is not suitable to detect variable adjacent density clusters due to the use of global density parameter neighborhood radius Y,.ad and minimum number of points in neighborhood Np~,. So the efficiency of DBSCAN depends on these initial parameter settings, for DBSCAN to work properly, the neighborhood radius must be less than the distance between two clusters otherwise algorithm merges two clusters and detects them as a single cluster. Through this paper： 1） We have proposed improved version of DBSCAN algorithm to detect clusters of varying density adjacent clusters by using the concept of neighborhood difference and using the notion of density based approach without introducing much additional computational complexity to original DBSCAN algorithm. 2） We validated our experimental results using one of our authors recently proposed space density indexing （SDI） internal cluster measure to demonstrate the quality of proposed clustering method. Also our experimental results suggested that proposed method is effective in detecting variable density adjacent nested clusters.
