Graph Sampling for Linked Data
收藏DataCite Commons2020-09-05 更新2024-07-25 收录
下载链接：
https://figshare.com/articles/dataset/Subgraph_Selection_for_Linked_Data/697539/4
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资源简介：
Results for Graph Sampling for Linked Data, a submission for ISWC 2013 We apply different rewrite methods to transform our RDF graph into an unweighted directed graph with unlabelled edges. The rewritten graph is analysed using different standard network algorithms, after which the weights are aggregated back to the RDF triples. We measure the quality of the subgraph using query logs, where we calculate the recall by executing each query on the subgraph as well as the original graph. Navigatable results (i.e. html) are available here:<br>http://data2semantics.github.io/GraphSampling/
面向关联数据的图采样研究成果——2013年国际语义网大会（ISWC）投稿论文相关结果。本研究采用多种重写方法，将目标资源描述框架（Resource Description Framework, RDF）图转换为无标注边的无权有向图。随后通过各类标准网络分析算法对重写后的图开展分析，并将计算所得权重回溯聚合至RDF三元组中。我们借助查询日志评估子图质量：分别在该子图与原始图上执行每一条查询，以此计算召回率（Recall）。可浏览的HTML格式结果可通过以下链接获取：http://data2semantics.github.io/GraphSampling/
提供机构：
figshare
创建时间：
2016-01-11
