Graph Sampling for Linked Data: SP2Bench Results
收藏DataCite Commons2020-09-05 更新2024-07-25 收录
下载链接：
https://figshare.com/articles/dataset/Graph_Sampling_for_Linked_Data_SP2Bench_Results/700714/1
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资源简介：
Results for Graph Sampling for Linked Data, a submission for ISWC 2013<br>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.<br>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.<br>Navigatable results (i.e. html) are available here:<br>http://data2semantics.github.io/GraphSampling/
本成果为面向关联数据的图采样方法研究，系ISWC 2013的投稿作品。
我们采用多种重写方法，将目标RDF图（RDF Graph）转换为边无标签的非加权有向图。
对重写后的图应用各类标准网络分析算法后，将计算得到的权重回溯聚合至RDF三元组（RDF Triples）中。
我们通过查询日志评估子图质量：分别在子图与原始图上执行各查询语句，以此计算召回率。
可浏览的可视化结果（HTML格式）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
