To overcome the limitation of the traditional clustering algorithms which fail to produce meaningful clusters in high-dimensional, sparseness and binary value data sets, a new method based on hypergraph model is propo...To overcome the limitation of the traditional clustering algorithms which fail to produce meaningful clusters in high-dimensional, sparseness and binary value data sets, a new method based on hypergraph model is proposed. The hypergraph model maps the relationship present in the original data in high dimensional space into a hypergraph. A hyperedge represents the similarity of attrlbute-value distribution between two points. A hypergraph partitioning algorithm is used to find a partitioning of the vertices such that the corresponding data items in each partition are highly related and the weight of the hyperedges cut by the partitioning is minimized. The quality of the clustering result can be evaluated by applying the intra-cluster singularity value. Analysis and experimental results have demonstrated that this approach is applicable and effective in wide ranging scheme.展开更多
The rapid development of Internet technology makes it possible to integrate GIS with the Internet,forming Internet GIS.Internet GIS is based on a distributed client/server architecture and TCP/IP & IIOP.When const...The rapid development of Internet technology makes it possible to integrate GIS with the Internet,forming Internet GIS.Internet GIS is based on a distributed client/server architecture and TCP/IP & IIOP.When constructing and designing Internet GIS,we face the problem of how to express information units of Internet GIS.In order to solve this problem,this paper presents a distributed hypermap model for Internet GIS.This model provides a solution to organize and manage Internet GIS information units.It also illustrates relations between two information units and in an internal information unit both on clients and servers.On the basis of this model,the paper contributes to the expressions of hypermap relations and hypermap operations.The usage of this model is shown in the implementation of a prototype system.展开更多
Practical real-world scenarios such as the Internet,social networks,and biological networks present the challenges of data scarcity and complex correlations,which limit the applications of artificial intelligence.The ...Practical real-world scenarios such as the Internet,social networks,and biological networks present the challenges of data scarcity and complex correlations,which limit the applications of artificial intelligence.The graph structure is a typical tool used to formulate such correlations,it is incapable of modeling highorder correlations among different objects in systems;thus,the graph structure cannot fully convey the intricate correlations among objects.Confronted with the aforementioned two challenges,hypergraph computation models high-order correlations among data,knowledge,and rules through hyperedges and leverages these high-order correlations to enhance the data.Additionally,hypergraph computation achieves collaborative computation using data and high-order correlations,thereby offering greater modeling flexibility.In particular,we introduce three types of hypergraph computation methods:①hypergraph structure modeling,②hypergraph semantic computing,and③efficient hypergraph computing.We then specify how to adopt hypergraph computation in practice by focusing on specific tasks such as three-dimensional(3D)object recognition,revealing that hypergraph computation can reduce the data requirement by 80%while achieving comparable performance or improve the performance by 52%given the same data,compared with a traditional data-based method.A comprehensive overview of the applications of hypergraph computation in diverse domains,such as intelligent medicine and computer vision,is also provided.Finally,we introduce an open-source deep learning library,DeepHypergraph(DHG),which can serve as a tool for the practical usage of hypergraph computation.展开更多
Cloud storage has the characteristics of distributed and virtual, and it makes the ownership rights and management rights of users data separated. The master-slave architecture of cloud storage has a problem of single...Cloud storage has the characteristics of distributed and virtual, and it makes the ownership rights and management rights of users data separated. The master-slave architecture of cloud storage has a problem of single point failure. In this paper, we provide a cloud storage architecture model based on Semantic equivalence. According to semantic matching degree, this architecture divides the nodes into node cluster by creating semantic tree and maintains system routing through semantic hypergraph. Through simulation experiments show that dividing network into semantic can enhance scalability and flexibility of the system, and it can improve the efficiency of network organization and the security of cloud storage system, at the same time, it can also reduce the cloud data storage and the delay of reading time.展开更多
知识超图(knowledge hypergraph,KHG)是超图结构的知识图谱。现有知识超图链接预测模型主要存在以下不足:模型输入时将实体和关系简单地表示为嵌入层的ID(索引),而没有考虑实体和关系之间复杂的联系和语义;编码时只考虑位置和角色信息,...知识超图(knowledge hypergraph,KHG)是超图结构的知识图谱。现有知识超图链接预测模型主要存在以下不足:模型输入时将实体和关系简单地表示为嵌入层的ID(索引),而没有考虑实体和关系之间复杂的联系和语义;编码时只考虑位置和角色信息,而忽略了实体邻域结构和多元关系间的联系,导致实体和关系表示能力不足;模型训练时采样的负样本质量不够高,不能帮助模型高效学习样本特征。针对以上问题,提出一种联合预训练模型和层级注意力的知识超图链接预测模型(link prediction in knowledge hypergraph combining pretrained model and hierarchical attention,LPPH)。该模型引入预训练模型和简化的团式展开方法初始化超图嵌入,将实体和关系之间复杂联系和语义融入至实体和关系嵌入中;编码时使用层级注意力机制聚合实体邻域结构信息以增强实体表示,并使用实体-关系融合操作增强关系表示;提出一种基于过滤机制和主动学习的负样本选择策略,实现模型的高效训练。真实数据集上的大量实验结果验证了LPPH能有效提高知识超图链接预测的效果。展开更多
在许多KDD(knowledge discovery in databases)应用中,如电子商务中的欺诈行为监测,例外情况或离群点的发现比常规知识的发现更有意义.现有的离群点发现大多是针对数值属性的,而且这些方法只能发现离群点不能对其含义进行解释.提出了一...在许多KDD(knowledge discovery in databases)应用中,如电子商务中的欺诈行为监测,例外情况或离群点的发现比常规知识的发现更有意义.现有的离群点发现大多是针对数值属性的,而且这些方法只能发现离群点不能对其含义进行解释.提出了一种基于超图模型的离群点(outlier)定义,这一定义既体现了“局部”的概念能很好地解释离群点的含义.同时给出了HOT(hypergraph-based outlier test)算法,通过计算每个点的支持度、隶属度和规模偏差来检测离群点.该算法既能够处理数值属性,又能够处理类别属性.分析表明,该算法能有效地发现高维空间数据中的离群点.展开更多
文摘To overcome the limitation of the traditional clustering algorithms which fail to produce meaningful clusters in high-dimensional, sparseness and binary value data sets, a new method based on hypergraph model is proposed. The hypergraph model maps the relationship present in the original data in high dimensional space into a hypergraph. A hyperedge represents the similarity of attrlbute-value distribution between two points. A hypergraph partitioning algorithm is used to find a partitioning of the vertices such that the corresponding data items in each partition are highly related and the weight of the hyperedges cut by the partitioning is minimized. The quality of the clustering result can be evaluated by applying the intra-cluster singularity value. Analysis and experimental results have demonstrated that this approach is applicable and effective in wide ranging scheme.
文摘The rapid development of Internet technology makes it possible to integrate GIS with the Internet,forming Internet GIS.Internet GIS is based on a distributed client/server architecture and TCP/IP & IIOP.When constructing and designing Internet GIS,we face the problem of how to express information units of Internet GIS.In order to solve this problem,this paper presents a distributed hypermap model for Internet GIS.This model provides a solution to organize and manage Internet GIS information units.It also illustrates relations between two information units and in an internal information unit both on clients and servers.On the basis of this model,the paper contributes to the expressions of hypermap relations and hypermap operations.The usage of this model is shown in the implementation of a prototype system.
文摘Practical real-world scenarios such as the Internet,social networks,and biological networks present the challenges of data scarcity and complex correlations,which limit the applications of artificial intelligence.The graph structure is a typical tool used to formulate such correlations,it is incapable of modeling highorder correlations among different objects in systems;thus,the graph structure cannot fully convey the intricate correlations among objects.Confronted with the aforementioned two challenges,hypergraph computation models high-order correlations among data,knowledge,and rules through hyperedges and leverages these high-order correlations to enhance the data.Additionally,hypergraph computation achieves collaborative computation using data and high-order correlations,thereby offering greater modeling flexibility.In particular,we introduce three types of hypergraph computation methods:①hypergraph structure modeling,②hypergraph semantic computing,and③efficient hypergraph computing.We then specify how to adopt hypergraph computation in practice by focusing on specific tasks such as three-dimensional(3D)object recognition,revealing that hypergraph computation can reduce the data requirement by 80%while achieving comparable performance or improve the performance by 52%given the same data,compared with a traditional data-based method.A comprehensive overview of the applications of hypergraph computation in diverse domains,such as intelligent medicine and computer vision,is also provided.Finally,we introduce an open-source deep learning library,DeepHypergraph(DHG),which can serve as a tool for the practical usage of hypergraph computation.
基金supported in part by the National Science and technology support program of China No. 2014BAH29F05the National High-Tech R&D Program (863 Program) No. 2015AA01A705+3 种基金the National Natural Science Foundation of China under Grant No. 61572072the National Science and Technology Major Project No. 2015ZX03001041the Fundamental Research Funds for the Central Universities No. FRF-TP-14-046A2"Research on the System of Personalized Education using Big Data"
文摘Cloud storage has the characteristics of distributed and virtual, and it makes the ownership rights and management rights of users data separated. The master-slave architecture of cloud storage has a problem of single point failure. In this paper, we provide a cloud storage architecture model based on Semantic equivalence. According to semantic matching degree, this architecture divides the nodes into node cluster by creating semantic tree and maintains system routing through semantic hypergraph. Through simulation experiments show that dividing network into semantic can enhance scalability and flexibility of the system, and it can improve the efficiency of network organization and the security of cloud storage system, at the same time, it can also reduce the cloud data storage and the delay of reading time.
文摘知识超图(knowledge hypergraph,KHG)是超图结构的知识图谱。现有知识超图链接预测模型主要存在以下不足:模型输入时将实体和关系简单地表示为嵌入层的ID(索引),而没有考虑实体和关系之间复杂的联系和语义;编码时只考虑位置和角色信息,而忽略了实体邻域结构和多元关系间的联系,导致实体和关系表示能力不足;模型训练时采样的负样本质量不够高,不能帮助模型高效学习样本特征。针对以上问题,提出一种联合预训练模型和层级注意力的知识超图链接预测模型(link prediction in knowledge hypergraph combining pretrained model and hierarchical attention,LPPH)。该模型引入预训练模型和简化的团式展开方法初始化超图嵌入,将实体和关系之间复杂联系和语义融入至实体和关系嵌入中;编码时使用层级注意力机制聚合实体邻域结构信息以增强实体表示,并使用实体-关系融合操作增强关系表示;提出一种基于过滤机制和主动学习的负样本选择策略,实现模型的高效训练。真实数据集上的大量实验结果验证了LPPH能有效提高知识超图链接预测的效果。
