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KGCW 2024 Challenge @ ESWC 2024
<h1><strong>Knowledge Graph Construction Workshop 2024: challenge</strong></h1> <p>Knowledge graph construction of heterogeneous data has seen a lot of uptake<br>in the last decade from compliance to performance optimizations with respect<br>to execution time. Besides execution time as a metric for comparing knowledge<br>graph construction, other metrics e.g. CPU or memory usage are not considered.<br>This challenge aims at benchmarking systems to find which RDF graph<br>construction system optimizes for metrics e.g. execution time, CPU,<br>memory usage, or a combination of these metrics.</p> <p><strong>Task description</strong></p> <p>The task is to reduce and report the execution time and computing resources<br>(CPU and memory usage) for the parameters listed in this challenge, compared<br>to the state-of-the-art of the existing tools and the baseline results provided<br>by this challenge. This challenge is not limited to execution times to create<br>the fastest pipeline, but also computing resources to achieve the most efficient<br>pipeline.</p> <p>We provide a tool which can execute such pipelines end-to-end. This tool also<br>collects and aggregates the metrics such as execution time, CPU and memory<br>usage, necessary for this challenge as CSV files. Moreover, the information<br>about the hardware used during the execution of the pipeline is available as<br>well to allow fairly comparing different pipelines. Your pipeline should consist<br>of Docker images which can be executed on Linux to run the tool. The tool is<br>already tested with existing systems, relational databases e.g. MySQL and<br>PostgreSQL, and triplestores e.g. Apache Jena Fuseki and OpenLink Virtuoso<br>which can be combined in any configuration. It is strongly encouraged to use<br>this tool for participating in this challenge. If you prefer to use a different<br>tool or our tool imposes technical requirements you cannot solve, please contact<br>us directly.</p> <h2><strong>Track 1: Conformance<br></strong></h2> <p>The set of new specification for the RDF Mapping Language (RML) established by the W3C Community Group on Knowledge Graph Construction provide a set of test-cases for each module:</p> <ul> <li> <p><a href="https://github.com/kg-construct/rml-core/">RML-Core</a></p> </li> <li> <p><a href="https://github.com/kg-construct/rml-io/">RML-IO</a></p> </li> <li> <p><a href="https://github.com/kg-construct/rml-cc/">RML-CC</a></p> </li> <li> <p><a href="https://github.com/kg-construct/rml-fnml/">RML-FNML</a></p> </li> <li> <p><a href="https://github.com/kg-construct/rml-star/">RML-Star</a></p> </li> </ul> <p>These test-cases are evaluated in this Track of the Challenge to determine their <strong>feasibility</strong>, correctness, etc. by applying them in implementations. <strong>This Track is in Beta status because these new specifications have not seen any implementation yet, thus it may contain bugs and issues.</strong> <strong>If you find problems with the mappings, output, etc. please report them to the corresponding repository of each module.</strong></p> <p><strong>Note:</strong> validating the output of the RML Star module automatically through the provided tooling is currently not possible, see <a href="https://github.com/kg-construct/challenge-tool/issues/1" target="_blank" rel="noopener">https://github.com/kg-construct/challenge-tool/issues/1</a>.</p> <p>Through this Track we aim to spark development of implementations for the new specifications and improve the test-cases. Let us know your problems with the test-cases and we will try to find a solution.</p> <h2><strong>Track 2: Performance</strong></h2> <p><strong>Part 1: Knowledge Graph Construction Parameters</strong></p> <p>These parameters are evaluated using synthetic generated data to have more<br>insights of their influence on the pipeline.</p> <p><em><strong>Data</strong></em></p> <ul> <li>Number of data records: scaling the data size vertically by the number of records with a fixed number of data properties (10K, 100K, 1M, 10M records).</li> <li>Number of data properties: scaling the data size horizontally by the number of data properties with a fixed number of data records (1, 10, 20, 30 columns).</li> <li>Number of duplicate values: scaling the number of duplicate values in the dataset (0%, 25%, 50%, 75%, 100%).</li> <li>Number of empty values: scaling the number of empty values in the dataset (0%, 25%, 50%, 75%, 100%).</li> <li>Number of input files: scaling the number of datasets (1, 5, 10, 15).</li> </ul> <p><em><strong>Mappings</strong></em></p> <ul> <li>Number of subjects: scaling the number of subjects with a fixed number of predicates and objects (1, 10, 20, 30 TMs).</li> <li>Number of predicates and objects: scaling the number of predicates and objects with a fixed number of subjects (1, 10, 20, 30 POMs).</li> <li>Number of and type of joins: scaling the number of joins and type of joins (1-1, N-1, 1-N, N-M)</li> </ul> <p><strong>Part 2: GTFS-Madrid-Bench</strong></p> <p>The GTFS-Madrid-Bench provides insights in the pipeline with real data from the<br>public transport domain in Madrid.</p> <p><em><strong>Scaling</strong></em></p> <ul> <li>GTFS-1 SQL</li> <li>GTFS-10 SQL</li> <li>GTFS-100 SQL</li> <li>GTFS-1000 SQL</li> </ul> <p><em><strong>Heterogeneity</strong></em></p> <ul> <li>GTFS-100 XML + JSON</li> <li>GTFS-100 CSV + XML</li> <li>GTFS-100 CSV + JSON</li> <li>GTFS-100 SQL + XML + JSON + CSV</li> </ul> <p><strong>Example pipeline</strong></p> <p>The ground truth dataset and baseline results are generated in different steps<br>for each parameter:</p> <ol> <li>The provided CSV files and SQL schema are loaded into a MySQL relational database.</li> <li>Mappings are executed by accessing the MySQL relational database to construct a knowledge graph in N-Triples as RDF format</li> </ol> <p>The pipeline is executed 5 times from which the median execution time of each<br>step is calculated and reported. Each step with the median execution time is<br>then reported in the baseline results with all its measured metrics.<br>Knowledge graph construction timeout is set to 24 hours. <br>The execution is performed with the following tool: <a href="https://github.com/kg-construct/challenge-tool">https://github.com/kg-construct/challenge-tool</a>,<br>you can adapt the execution plans for this example pipeline to your own needs.</p> <p>Each parameter has its own directory in the ground truth dataset with the<br>following files:</p> <ul> <li>Input dataset as CSV.</li> <li>Mapping file as RML.</li> <li>Execution plan for the pipeline in <code>metadata.json</code>.</li> </ul> <p><strong>Datasets</strong></p> <p><em><strong>Knowledge Graph Construction Parameters</strong></em></p> <p>The dataset consists of:</p> <ul> <li>Input dataset as CSV for each parameter.</li> <li>Mapping file as RML for each parameter.</li> <li>Baseline results for each parameter with the example pipeline.</li> <li>Ground truth dataset for each parameter generated with the example pipeline.</li> </ul> <p><em>Format</em></p> <p>All input datasets are provided as CSV, depending on the parameter that is being<br>evaluated, the number of rows and columns may differ. The first row is always<br>the header of the CSV.</p> <p><em><strong>GTFS-Madrid-Bench</strong></em></p> <p>The dataset consists of:</p> <ul> <li>Input dataset as CSV with SQL schema for the scaling and a combination of XML,</li> <li>CSV, and JSON is provided for the heterogeneity.</li> <li>Mapping file as RML for both scaling and heterogeneity.</li> <li>SPARQL queries to retrieve the results.</li> <li>Baseline results with the example pipeline.</li> <li>Ground truth dataset generated with the example pipeline.</li> </ul> <p><em>Format</em></p> <p>CSV datasets always have a header as their first row.<br>JSON and XML datasets have their own schema.</p> <p><strong>Evaluation criteria</strong></p> <p>Submissions must evaluate the following metrics:</p> <ul> <li>Execution time of all the steps in the pipeline. The execution time of a step is the difference between the begin and end time of a step.</li> <li>CPU time as the time spent in the CPU for all steps of the pipeline. The CPU time of a step is the difference between the begin and end CPU time of a step.</li> <li>Minimal and maximal memory consumption for each step of the pipeline. The minimal and maximal memory consumption of a step is the minimum and maximum calculated of the memory consumption during the execution of a step.</li> </ul> <p><strong>Expected output</strong></p> <p><em><strong>Duplicate values</strong></em></p> <table> <tbody> <tr> <th>Scale</th> <th>Number of Triples</th> </tr> </tbody> <tbody> <tr> <td>0 percent</td> <td>2000000 triples</td> </tr> <tr> <td>25 percent</td> <td>1500020 triples </td> </tr> <tr> <td>50 percent</td> <td>1000020 triples </td> </tr> <tr> <td>75 percent</td> <td>500020 triples</td> </tr> <tr> <td>100 percent</td> <td>20 triples</td> </tr> </tbody> </table> <p> </p> <p><em><strong>Empty values</strong></em></p> <p> </p> <table> <tbody> <tr> <th>Scale</th> <th>Number of Triples</th> </tr> </tbody> <tbody> <tr> <td>0 percent</td> <td>2000000 triples</td> </tr> <tr> <td>25 percent</td> <td>1500000 triples </td> </tr> <tr> <td>50 percent</td> <td>1000000 triples </td> </tr> <tr> <td>75 percent</td> <td>500000 triples</td> </tr> <tr> <td>100 percent</td> <td>0 triples</td> </tr> </tbody> </table> <p><em><strong>Mappings</strong></em></p> <table> <tbody> <tr> <th>Scale</th> <th>Number of Triples</th> </tr> </tbody> <tbody> <tr> <td>1TM + 15POM</td> <td>1500000 triples</td> </tr> <tr> <td>3TM + 5POM</td> <td>1500000 triples </td> </tr> <tr> <td>5TM + 3POM </td> <td>1500000 triples </td> </tr> <tr> <td>15TM + 1POM</td> <td>1500000 triples</td> </tr> </tbody> </table> <p><em><strong>Properties</strong></em></p> <table> <tbody> <tr> <th>Scale</th> <th>Number of Triples</th> </tr> <tr> <td>1M rows 1 column</td> <td>1000000 triples</td> </tr> <tr> <td>1M rows 10 columns</td> <td>10000000 triples </td> </tr> <tr> <td>1M rows 20 columns</td> <td>20000000 triples </td> </tr> <tr> <td>1M rows 30 columns</td> <td>30000000 triples</td> </tr> </tbody> </table> <p><em><strong>Records</strong></em></p> <table> <tbody> <tr> <th>Scale</th> <th>Number of Triples</th> </tr> <tr> <td>10K rows 20 columns</td> <td>200000 triples</td> </tr> <tr> <td>100K rows 20 columns</td> <td>2000000 triples </td> </tr> <tr> <td>1M rows 20 columns</td> <td>20000000 triples </td> </tr> <tr> <td>10M rows 20 columns</td> <td>200000000 triples</td> </tr> </tbody> </table> <p><em><strong>Joins</strong></em></p> <p><em>1-1 joins</em></p> <table> <tbody> <tr> <th>Scale</th> <th>Number of Triples</th> </tr> <tr> <td>0 percent</td> <td>0 triples</td> </tr> <tr> <td>25 percent</td> <td>125000 triples </td> </tr> <tr> <td>50 percent</td> <td>250000 triples </td> </tr> <tr> <td>75 percent</td> <td>375000 triples</td> </tr> <tr> <td>100 percent</td> <td>500000 triples</td> </tr> </tbody> </table> <p><em>1-N joins</em></p> <table> <tbody> <tr> <th>Scale</th> <th>Number of Triples</th> </tr> <tr> <td>1-10 0 percent</td> <td>0 triples</td> </tr> <tr> <td>1-10 25 percent</td> <td>125000 triples </td> </tr> <tr> <td>1-10 50 percent</td> <td>250000 triples </td> </tr> <tr> <td>1-10 75 percent</td> <td>375000 triples</td> </tr> <tr> <td>1-10 100 percent</td> <td>500000 triples</td> </tr> <tr> <td>1-5 50 percent</td> <td>250000 triples</td> </tr> <tr> <td>1-10 50 percent</td> <td>250000 triples </td> </tr> <tr> <td>1-15 50 percent</td> <td>250005 triples </td> </tr> <tr> <td>1-20 50 percent</td> <td>250000 triples</td> </tr> </tbody> </table> <p><em>1-N joins</em></p> <table> <tbody> <tr> <th>Scale</th> <th>Number of Triples</th> </tr> <tr> <td>10-1 0 percent</td> <td>0 triples</td> </tr> <tr> <td>10-1 25 percent</td> <td>125000 triples </td> </tr> <tr> <td>10-1 50 percent</td> <td>250000 triples </td> </tr> <tr> <td>10-1 75 percent</td> <td>375000 triples</td> </tr> <tr> <td>10-1 100 percent</td> <td>500000 triples</td> </tr> <tr> <td>5-1 50 percent</td> <td>250000 triples</td> </tr> <tr> <td>10-1 50 percent</td> <td>250000 triples </td> </tr> <tr> <td>15-1 50 percent</td> <td>250005 triples </td> </tr> <tr> <td>20-1 50 percent</td> <td>250000 triples</td> </tr> </tbody> </table> <p><em>N-M joins</em></p> <table> <tbody> <tr> <th>Scale</th> <th>Number of Triples</th> </tr> <tr> <td>5-5 50 percent</td> <td>1374085 triples</td> </tr> <tr> <td>10-5 50 percent</td> <td>1375185 triples</td> </tr> <tr> <td>5-10 50 percent </td> <td>1375290 triples</td> </tr> <tr> <td>5-5 25 percent</td> <td>718785 triples</td> </tr> <tr> <td>5-5 50 percent</td> <td>1374085 triples</td> </tr> <tr> <td>5-5 75 percent </td> <td>1968100 triples</td> </tr> <tr> <td>5-5 100 percent </td> <td>2500000 triples </td> </tr> <tr> <td>5-10 25 percent </td> <td>719310 triples</td> </tr> <tr> <td>5-10 50 percent </td> <td>1375290 triples</td> </tr> <tr> <td>5-10 75 percent </td> <td>1967660 triples</td> </tr> <tr> <td>5-10 100 percent </td> <td>2500000 triples</td> </tr> <tr> <td>10-5 25 percent </td> <td>719370 triples </td> </tr> <tr> <td>10-5 50 percent </td> <td>1375185 triples</td> </tr> <tr> <td>10-5 75 percent </td> <td>1968235 triples</td> </tr> <tr> <td>10-5 100 percent </td> <td>2500000 triples</td> </tr> </tbody> </table> <p><strong>GTFS Madrid Bench</strong></p> <p><em>Generated Knowledge Graph</em></p> <table> <tbody> <tr> <th>Scale</th> <th>Number of Triples</th> </tr> <tr> <td>1</td> <td>395953 triples</td> </tr> <tr> <td>10</td> <td>3959530 triples </td> </tr> <tr> <td>100</td> <td>39595300 triples </td> </tr> <tr> <td>1000</td> <td>395953000 triples</td> </tr> </tbody> </table> <p><em>Queries</em></p> <table> <tbody> <tr> <th>Query</th> <th>Scale 1</th> <th>Scale 10</th> <th>Scale 100</th> <th>Scale 1000</th> </tr> <tr> <td>Q1</td> <td>58540 results</td> <td>585400 results</td> <td>No results available</td> <td>No results available</td> </tr> <tr> <td>Q2</td> <td>636 results</td> <td>11998 results </td> <td>125565 results</td> <td>1261368 results</td> </tr> <tr> <td>Q3</td> <td>421 results</td> <td>4207 results </td> <td>42067 results</td> <td>420667 results</td> </tr> <tr> <td>Q4</td> <td>13 results</td> <td>130 results</td> <td>1300 results</td> <td>13000 results</td> </tr> <tr> <td>Q5</td> <td>35 results</td> <td>350 results</td> <td>3500 results</td> <td>35000 results</td> </tr> <tr> <td>Q6</td> <td>1 result</td> <td>1 result</td> <td>1 result</td> <td>1 result</td> </tr> <tr> <td>Q7</td> <td>68 results</td> <td>67 results</td> <td>67 results</td> <td>53 results</td> </tr> <tr> <td>Q8</td> <td>35460 results</td> <td>354600 results</td> <td>No results available</td> <td>No results available</td> </tr> <tr> <td>Q9</td> <td>130 results</td> <td>1300 results</td> <td>13000 results</td> <td>130000 results</td> </tr> <tr> <td>Q10</td> <td>1 result</td> <td>1 result</td> <td>1 result</td> <td>1 result</td> </tr> <tr> <td>Q11</td> <td>130 results</td> <td>260 results </td> <td>260 results </td> <td>260 results </td> </tr> <tr> <td>Q12</td> <td>13 results</td> <td>130 results</td> <td>1300 results</td> <td>13000 results</td> </tr> <tr> <td>Q13</td> <td>265 results</td> <td>2650 results</td> <td>26500 results</td> <td>265000 results</td> </tr> <tr> <td>Q14</td> <td>2234 results</td> <td>22340 results</td> <td>223400 results</td> <td>No results available</td> </tr> <tr> <td>Q15</td> <td>592 results</td> <td>8684 results</td> <td>35502 results</td> <td>206628 results</td> </tr> <tr> <td>Q16</td> <td>390 results</td> <td>780 results</td> <td>260 results</td> <td>780 results</td> </tr> <tr> <td>Q17</td> <td>855 results</td> <td>8550 results </td> <td>85500 results </td> <td>855000 results </td> </tr> <tr> <td>Q18</td> <td>104 results</td> <td>1300 results</td> <td>13000 results</td> <td>130000 results</td> </tr> </tbody> </table> <p> </p>
KGCW 2023 Challenge @ ESWC 2023
<h1><strong>Knowledge Graph Construction Workshop 2023: challenge</strong></h1> <p>Knowledge graph construction of heterogeneous data has seen a lot of uptake<br>in the last decade from compliance to performance optimizations with respect<br>to execution time. Besides execution time as a metric for comparing knowledge<br>graph construction, other metrics e.g. CPU or memory usage are not considered.<br>This challenge aims at benchmarking systems to find which RDF graph<br>construction system optimizes for metrics e.g. execution time, CPU,<br>memory usage, or a combination of these metrics.</p> <p><strong>Task description</strong></p> <p>The task is to reduce and report the execution time and computing resources<br>(CPU and memory usage) for the parameters listed in this challenge, compared<br>to the state-of-the-art of the existing tools and the baseline results provided<br>by this challenge. This challenge is not limited to execution times to create<br>the fastest pipeline, but also computing resources to achieve the most efficient<br>pipeline.</p> <p>We provide a tool which can execute such pipelines end-to-end. This tool also<br>collects and aggregates the metrics such as execution time, CPU and memory<br>usage, necessary for this challenge as CSV files. Moreover, the information<br>about the hardware used during the execution of the pipeline is available as<br>well to allow fairly comparing different pipelines. Your pipeline should consist<br>of Docker images which can be executed on Linux to run the tool. The tool is<br>already tested with existing systems, relational databases e.g. MySQL and<br>PostgreSQL, and triplestores e.g. Apache Jena Fuseki and OpenLink Virtuoso<br>which can be combined in any configuration. It is strongly encouraged to use<br>this tool for participating in this challenge. If you prefer to use a different<br>tool or our tool imposes technical requirements you cannot solve, please contact<br>us directly.</p> <p><strong>Part 1: Knowledge Graph Construction Parameters</strong></p> <p>These parameters are evaluated using synthetic generated data to have more<br>insights of their influence on the pipeline.</p> <p><em><strong>Data</strong></em></p> <ul> <li>Number of data records: scaling the data size vertically by the number of records with a fixed number of data properties (10K, 100K, 1M, 10M records).</li> <li>Number of data properties: scaling the data size horizontally by the number of data properties with a fixed number of data records (1, 10, 20, 30 columns).</li> <li>Number of duplicate values: scaling the number of duplicate values in the dataset (0%, 25%, 50%, 75%, 100%).</li> <li>Number of empty values: scaling the number of empty values in the dataset (0%, 25%, 50%, 75%, 100%).</li> <li>Number of input files: scaling the number of datasets (1, 5, 10, 15).</li> </ul> <p><em><strong>Mappings</strong></em></p> <ul> <li>Number of subjects: scaling the number of subjects with a fixed number of predicates and objects (1, 10, 20, 30 TMs).</li> <li>Number of predicates and objects: scaling the number of predicates and objects with a fixed number of subjects (1, 10, 20, 30 POMs).</li> <li>Number of and type of joins: scaling the number of joins and type of joins (1-1, N-1, 1-N, N-M)</li> </ul> <p><strong>Part 2: GTFS-Madrid-Bench</strong></p> <p>The GTFS-Madrid-Bench provides insights in the pipeline with real data from the<br>public transport domain in Madrid.</p> <p><em><strong>Scaling</strong></em></p> <ul> <li>GTFS-1 SQL</li> <li>GTFS-10 SQL</li> <li>GTFS-100 SQL</li> <li>GTFS-1000 SQL</li> </ul> <p><em><strong>Heterogeneity</strong></em></p> <ul> <li>GTFS-100 XML + JSON</li> <li>GTFS-100 CSV + XML</li> <li>GTFS-100 CSV + JSON</li> <li>GTFS-100 SQL + XML + JSON + CSV</li> </ul> <p><strong>Example pipeline</strong></p> <p>The ground truth dataset and baseline results are generated in different steps<br>for each parameter:</p> <ol> <li>The provided CSV files and SQL schema are loaded into a MySQL relational database.</li> <li>Mappings are executed by accessing the MySQL relational database to construct a knowledge graph in N-Triples as RDF format.</li> <li>The constructed knowledge graph is loaded into a Virtuoso triplestore, tuned according to the Virtuoso documentation.</li> <li>The provided SPARQL queries are executed on the SPARQL endpoint exposed by Virtuoso.</li> </ol> <p>The pipeline is executed 5 times from which the median execution time of each<br>step is calculated and reported. Each step with the median execution time is<br>then reported in the baseline results with all its measured metrics.<br>Query timeout is set to 1 hour and knowledge graph construction timeout<br>to 24 hours. The execution is performed with the following tool: <a href="https://github.com/kg-construct/challenge-tool">https://github.com/kg-construct/challenge-tool</a>,<br>you can adapt the execution plans for this example pipeline to your own needs.</p> <p>Each parameter has its own directory in the ground truth dataset with the<br>following files:</p> <ul> <li>Input dataset as CSV.</li> <li>Mapping file as RML.</li> <li>Queries as SPARQL.</li> <li>Execution plan for the pipeline in <code>metadata.json</code>.</li> </ul> <p><strong>Datasets</strong></p> <p><em><strong>Knowledge Graph Construction Parameters</strong></em></p> <p>The dataset consists of:</p> <ul> <li>Input dataset as CSV for each parameter.</li> <li>Mapping file as RML for each parameter.</li> <li>SPARQL queries to retrieve the results for each parameter.</li> <li>Baseline results for each parameter with the example pipeline.</li> <li>Ground truth dataset for each parameter generated with the example pipeline.</li> </ul> <p><em>Format</em></p> <p>All input datasets are provided as CSV, depending on the parameter that is being<br>evaluated, the number of rows and columns may differ. The first row is always<br>the header of the CSV.</p> <p><em><strong>GTFS-Madrid-Bench</strong></em></p> <p>The dataset consists of:</p> <ul> <li>Input dataset as CSV with SQL schema for the scaling and a combination of XML,</li> <li>CSV, and JSON is provided for the heterogeneity.</li> <li>Mapping file as RML for both scaling and heterogeneity.</li> <li>SPARQL queries to retrieve the results.</li> <li>Baseline results with the example pipeline.</li> <li>Ground truth dataset generated with the example pipeline.</li> </ul> <p><em>Format</em></p> <p>CSV datasets always have a header as their first row.<br>JSON and XML datasets have their own schema.</p> <p><strong>Evaluation criteria</strong></p> <p>Submissions must evaluate the following metrics:</p> <ul> <li>Execution time of all the steps in the pipeline. The execution time of a step is the difference between the begin and end time of a step.</li> <li>CPU time as the time spent in the CPU for all steps of the pipeline. The CPU time of a step is the difference between the begin and end CPU time of a step.</li> <li>Minimal and maximal memory consumption for each step of the pipeline. The minimal and maximal memory consumption of a step is the minimum and maximum calculated of the memory consumption during the execution of a step.</li> </ul> <p><strong>Expected output</strong></p> <p><em><strong>Duplicate values</strong></em></p> <table> <tbody><tr> <th>Scale</th> <th>Number of Triples</th> </tr> </tbody><tbody> <tr> <td>0 percent</td> <td>2000000 triples</td> </tr> <tr> <td>25 percent</td> <td>1500020 triples </td> </tr> <tr> <td>50 percent</td> <td>1000020 triples </td> </tr> <tr> <td>75 percent</td> <td>500020 triples</td> </tr> <tr> <td>100 percent</td> <td>20 triples</td> </tr> </tbody> </table> <p> </p> <p><em><strong>Empty values</strong></em></p> <p> </p> <table> <tbody><tr> <th>Scale</th> <th>Number of Triples</th> </tr> </tbody><tbody> <tr> <td>0 percent</td> <td>2000000 triples</td> </tr> <tr> <td>25 percent</td> <td>1500000 triples </td> </tr> <tr> <td>50 percent</td> <td>1000000 triples </td> </tr> <tr> <td>75 percent</td> <td>500000 triples</td> </tr> <tr> <td>100 percent</td> <td>0 triples</td> </tr> </tbody> </table> <p><em><strong>Mappings</strong></em></p> <table> <tbody><tr> <th>Scale</th> <th>Number of Triples</th> </tr> </tbody><tbody> <tr> <td>1TM + 15POM</td> <td>1500000 triples</td> </tr> <tr> <td>3TM + 5POM</td> <td>1500000 triples </td> </tr> <tr> <td>5TM + 3POM </td> <td>1500000 triples </td> </tr> <tr> <td>15TM + 1POM</td> <td>1500000 triples</td> </tr> </tbody> </table> <p><em><strong>Properties</strong></em></p> <table> <tbody><tr> <th>Scale</th> <th>Number of Triples</th> </tr> <tr> <td>1M rows 1 column</td> <td>1000000 triples</td> </tr> <tr> <td>1M rows 10 columns</td> <td>10000000 triples </td> </tr> <tr> <td>1M rows 20 columns</td> <td>20000000 triples </td> </tr> <tr> <td>1M rows 30 columns</td> <td>30000000 triples</td> </tr> </tbody></table> <p><em><strong>Records</strong></em></p> <table> <tbody><tr> <th>Scale</th> <th>Number of Triples</th> </tr> <tr> <td>10K rows 20 columns</td> <td>200000 triples</td> </tr> <tr> <td>100K rows 20 columns</td> <td>2000000 triples </td> </tr> <tr> <td>1M rows 20 columns</td> <td>20000000 triples </td> </tr> <tr> <td>10M rows 20 columns</td> <td>200000000 triples</td> </tr> </tbody></table> <p><em><strong>Joins</strong></em></p> <p><em>1-1 joins</em></p> <table> <tbody><tr> <th>Scale</th> <th>Number of Triples</th> </tr> <tr> <td>0 percent</td> <td>0 triples</td> </tr> <tr> <td>25 percent</td> <td>125000 triples </td> </tr> <tr> <td>50 percent</td> <td>250000 triples </td> </tr> <tr> <td>75 percent</td> <td>375000 triples</td> </tr> <tr> <td>100 percent</td> <td>500000 triples</td> </tr> </tbody></table> <p><em>1-N joins</em></p> <table> <tbody><tr> <th>Scale</th> <th>Number of Triples</th> </tr> <tr> <td>1-10 0 percent</td> <td>0 triples</td> </tr> <tr> <td>1-10 25 percent</td> <td>125000 triples </td> </tr> <tr> <td>1-10 50 percent</td> <td>250000 triples </td> </tr> <tr> <td>1-10 75 percent</td> <td>375000 triples</td> </tr> <tr> <td>1-10 100 percent</td> <td>500000 triples</td> </tr> <tr> <td>1-5 50 percent</td> <td>250000 triples</td> </tr> <tr> <td>1-10 50 percent</td> <td>250000 triples </td> </tr> <tr> <td>1-15 50 percent</td> <td>250005 triples </td> </tr> <tr> <td>1-20 50 percent</td> <td>250000 triples</td> </tr> </tbody></table> <p><em>1-N joins</em></p> <table> <tbody><tr> <th>Scale</th> <th>Number of Triples</th> </tr> <tr> <td>10-1 0 percent</td> <td>0 triples</td> </tr> <tr> <td>10-1 25 percent</td> <td>125000 triples </td> </tr> <tr> <td>10-1 50 percent</td> <td>250000 triples </td> </tr> <tr> <td>10-1 75 percent</td> <td>375000 triples</td> </tr> <tr> <td>10-1 100 percent</td> <td>500000 triples</td> </tr> <tr> <td>5-1 50 percent</td> <td>250000 triples</td> </tr> <tr> <td>10-1 50 percent</td> <td>250000 triples </td> </tr> <tr> <td>15-1 50 percent</td> <td>250005 triples </td> </tr> <tr> <td>20-1 50 percent</td> <td>250000 triples</td> </tr> </tbody></table> <p><em>N-M joins</em></p> <table> <tbody><tr> <th>Scale</th> <th>Number of Triples</th> </tr> <tr> <td>5-5 50 percent</td> <td>1374085 triples</td> </tr> <tr> <td>10-5 50 percent</td> <td>1375185 triples</td> </tr> <tr> <td>5-10 50 percent </td> <td>1375290 triples</td> </tr> <tr> <td>5-5 25 percent</td> <td>718785 triples</td> </tr> <tr> <td>5-5 50 percent</td> <td>1374085 triples</td> </tr> <tr> <td>5-5 75 percent </td> <td>1968100 triples</td> </tr> <tr> <td>5-5 100 percent </td> <td>2500000 triples </td> </tr> <tr> <td>5-10 25 percent </td> <td>719310 triples</td> </tr> <tr> <td>5-10 50 percent </td> <td>1375290 triples</td> </tr> <tr> <td>5-10 75 percent </td> <td>1967660 triples</td> </tr> <tr> <td>5-10 100 percent </td> <td>2500000 triples</td> </tr> <tr> <td>10-5 25 percent </td> <td>719370 triples </td> </tr> <tr> <td>10-5 50 percent </td> <td>1375185 triples</td> </tr> <tr> <td>10-5 75 percent </td> <td>1968235 triples</td> </tr> <tr> <td>10-5 100 percent </td> <td>2500000 triples</td> </tr> </tbody></table> <p><strong>GTFS Madrid Bench</strong></p> <p><em>Generated Knowledge Graph</em></p> <table> <tbody><tr> <th>Scale</th> <th>Number of Triples</th> </tr> <tr> <td>1</td> <td>395953 triples</td> </tr> <tr> <td>10</td> <td>3959530 triples </td> </tr> <tr> <td>100</td> <td>39595300 triples </td> </tr> <tr> <td>1000</td> <td>395953000 triples</td> </tr> </tbody></table> <p><em>Queries</em></p> <table> <tbody><tr> <th>Query</th> <th>Scale 1</th> <th>Scale 10</th> <th>Scale 100</th> <th>Scale 1000</th> </tr> <tr> <td>Q1</td> <td>58540 results</td> <td>585400 results</td> <td>No results available</td> <td>No results available</td> </tr> <tr> <td>Q2</td> <td>636 results</td> <td>11998 results </td> <td>125565 results</td> <td>1261368 results</td> </tr> <tr> <td>Q3</td> <td>421 results</td> <td>4207 results </td> <td>42067 results</td> <td>420667 results</td> </tr> <tr> <td>Q4</td> <td>13 results</td> <td>130 results</td> <td>1300 results</td> <td>13000 results</td> </tr> <tr> <td>Q5</td> <td>35 results</td> <td>350 results</td> <td>3500 results</td> <td>35000 results</td> </tr> <tr> <td>Q6</td> <td>1 result</td> <td>1 result</td> <td>1 result</td> <td>1 result</td> </tr> <tr> <td>Q7</td> <td>68 results</td> <td>67 results</td> <td>67 results</td> <td>53 results</td> </tr> <tr> <td>Q8</td> <td>35460 results</td> <td>354600 results</td> <td>No results available</td> <td>No results available</td> </tr> <tr> <td>Q9</td> <td>130 results</td> <td>1300 results</td> <td>13000 results</td> <td>130000 results</td> </tr> <tr> <td>Q10</td> <td>1 result</td> <td>1 result</td> <td>1 result</td> <td>1 result</td> </tr> <tr> <td>Q11</td> <td>130 results</td> <td>260 results </td> <td>260 results </td> <td>260 results </td> </tr> <tr> <td>Q12</td> <td>13 results</td> <td>130 results</td> <td>1300 results</td> <td>13000 results</td> </tr> <tr> <td>Q13</td> <td>265 results</td> <td>2650 results</td> <td>26500 results</td> <td>265000 results</td> </tr> <tr> <td>Q14</td> <td>2234 results</td> <td>22340 results</td> <td>223400 results</td> <td>No results available</td> </tr> <tr> <td>Q15</td> <td>592 results</td> <td>8684 results</td> <td>35502 results</td> <td>206628 results</td> </tr> <tr> <td>Q16</td> <td>390 results</td> <td>780 results</td> <td>260 results</td> <td>780 results</td> </tr> <tr> <td>Q17</td> <td>855 results</td> <td>8550 results </td> <td>85500 results </td> <td>855000 results </td> </tr> <tr> <td>Q18</td> <td>104 results</td> <td>1300 results</td> <td>13000 results</td> <td>130000 results</td> </tr> </tbody></table> <p> </p>
RMLStreamer with Reference Conditions in the KGCW Challenge 2023
<p>This repository contains all necessary data to reproduce the participation of RMLStreamer with reference conditions in the <a href="https://doi.org/10.5281/zenodo.7689310">KGCW 2023 Challenge</a>, as well as the reported results.</p> <p>For more information we refer to the related paper:</p> <p><em>E. de Vleeschauwer, G.Haesendonck, D. Van Assche. B. De Meester, RMLStreamer with Reference Conditions in the<br> KGCW Challenge 2023, in: Proceedings of the 4rd International Workshop on Knowledge Graph Construction (KGCW 2023) co-located with 20th Extended Semantic Web Conference (ESWC 2023), 2023</em></p> <p> </p> <p> </p> <p> </p>
RMLStreamer supported by RML-view-to-CSV in the performance track of the KGCW Challenge 2024
<p>This repository contains all necessary data to reproduce the participation of RMLStreamer with support of RML-view-to-CSV in the <a href="../records/10721875">KGCW 2024 Challenge</a>, as well as the reported results.</p> <p>For more information we refer to the related paper:</p> <p><em>E. de Vleeschauwer, B. De Meester, RMLStreamer supported by RML-view-to-CSV in the performance track of the KGCW Challenge 2024, in: Proceedings of the 5th International Workshop on Knowledge Graph Construction (KGCW 2024) co-located with 21th Extended Semantic Web Conference (ESWC 2024), 2024</em></p> <p> </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.