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A Fast Hop-Biased Approximation Algorithm for the Quadratic Group Steiner Tree Problem

<p>The dataset for our paper &#39;A Fast Hop-Biased Approximation Algorithm for the Quadratic Group Steiner Tree Problem&#39;. It consists of 5 real KGs (<code>Mondial</code>, <code>OpenCyc</code>, <code>LinkedMDB</code>, <code>YAGO</code>, <code>DBpedia</code>) and 5 synthetic KGs (<code>LUBM-10U</code>, <code>LUBM-50U</code>, <code>LUBM-250U</code>, <code>LUBM-2U</code>, <code>DBP-50K</code>). Each KG is compressed in one file, which including (for example, in <code>LUBM-2U</code>):</p> <ul> <li> <p><code>lubm_2u_nodes.sql</code>: the id, the name and the weight of a node,</p> </li> <li> <p><code>lubm_2u_edges.sql</code>: the ids of two nodes an edge connects,</p> </li> <li> <p><code>lubm_2u_queries.sql</code>: a query consists of some keywords,</p> </li> <li> <p><code>lubm_2u_keymap.sql</code>: a keyword maps to a set of nodes,</p> </li> <li> <p><code>lubm_2u_nodevec.sql</code>: the vector of a node, used to compute quadratic function qw,</p> </li> <li> <p><code>lubm_2u_hub_hop.sql</code>: the hub labeling index to compute in Section 4.1,</p> </li> <li> <p><code>lubm_2u_hub_mix_1.sql</code>: the hub labeling index to compute in Section 4.1 where &alpha;=0.1,</p> </li> <li> <p><code>lubm_2u_hub_mix_5.sql</code>: the hub labeling index to compute in Section 4.1 where &alpha;=0.5,</p> </li> <li> <p><code>lubm_2u_hub_mix_9.sql</code>: the hub labeling index to compute in Section 4.1 where &alpha;=0.9.</p> </li> </ul> <p>You can dump the data into MySQL database. For example,</p> <pre><code>create database lubm_2u; use lubm_2u; source lubm_2u_nodes.sql; …</code></pre> <p>Unfortunately, due to the limit of space, for large KGs (<code>DBpedia</code> and <code>LUBM-250U</code>), we don&#39;t directly provide the data of hub labeling, i.e., these two compressed files only contains the first 5 sql files. You should generate hub labeling by yourself where the process is detailed in <a href="https://github.com/nju-websoft/QGSTP-HB/blob/main/README.md">README</a> of our project.</p> <p>&nbsp;</p>

ShareScore

8/100

Overall dataset sharing score

Score breakdown

These five areas show where the dataset supports — or may limit — practical reuse.

Stewardship
4
Harmonization
4
Access
0
Reuse readiness
0
Engagement
0