Knowledge Graph Construction Workshop 2024: challenge

Knowledge graph construction of heterogeneous data has seen a lot of uptake
in the last decade from compliance to performance optimizations with respect
to execution time. Besides execution time as a metric for comparing knowledge
graph construction, other metrics e.g. CPU or memory usage are not considered.
This challenge aims at benchmarking systems to find which RDF graph
construction system optimizes for metrics e.g. execution time, CPU,
memory usage, or a combination of these metrics.

Task description

The task is to reduce and report the execution time and computing resources
(CPU and memory usage) for the parameters listed in this challenge, compared
to the state-of-the-art of the existing tools and the baseline results provided
by this challenge. This challenge is not limited to execution times to create
the fastest pipeline, but also computing resources to achieve the most efficient
pipeline.

We provide a tool which can execute such pipelines end-to-end. This tool also
collects and aggregates the metrics such as execution time, CPU and memory
usage, necessary for this challenge as CSV files. Moreover, the information
about the hardware used during the execution of the pipeline is available as
well to allow fairly comparing different pipelines. Your pipeline should consist
of Docker images which can be executed on Linux to run the tool. The tool is
already tested with existing systems, relational databases e.g. MySQL and
PostgreSQL, and triplestores e.g. Apache Jena Fuseki and OpenLink Virtuoso
which can be combined in any configuration. It is strongly encouraged to use
this tool for participating in this challenge. If you prefer to use a different
tool or our tool imposes technical requirements you cannot solve, please contact
us directly.

Track 1: Conformance

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:

• RML-Core

• RML-IO

• RML-CC

• RML-FNML

• RML-Star

These test-cases are evaluated in this Track of the Challenge to determine their feasibility, correctness, etc. by applying them in implementations. This Track is in Beta status because these new specifications have not seen any implementation yet, thus it may contain bugs and issues. If you find problems with the mappings, output, etc. please report them to the corresponding repository of each module.

Note: validating the output of the RML Star module automatically through the provided tooling is currently not possible, see https://github.com/kg-construct/challenge-tool/issues/1.

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.

Track 2: Performance

Part 1: Knowledge Graph Construction Parameters

These parameters are evaluated using synthetic generated data to have more
insights of their influence on the pipeline.

Data

• 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).
• 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).
• Number of duplicate values: scaling the number of duplicate values in the dataset (0%, 25%, 50%, 75%, 100%).
• Number of empty values: scaling the number of empty values in the dataset (0%, 25%, 50%, 75%, 100%).
• Number of input files: scaling the number of datasets (1, 5, 10, 15).

Mappings

• Number of subjects: scaling the number of subjects with a fixed number of predicates and objects (1, 10, 20, 30 TMs).
• Number of predicates and objects: scaling the number of predicates and objects with a fixed number of subjects (1, 10, 20, 30 POMs).
• Number of and type of joins: scaling the number of joins and type of joins (1-1, N-1, 1-N, N-M)

Part 2: GTFS-Madrid-Bench

The GTFS-Madrid-Bench provides insights in the pipeline with real data from the
public transport domain in Madrid.

Scaling

• GTFS-1 SQL
• GTFS-10 SQL
• GTFS-100 SQL
• GTFS-1000 SQL

Heterogeneity

• GTFS-100 XML + JSON
• GTFS-100 CSV + XML
• GTFS-100 CSV + JSON
• GTFS-100 SQL + XML + JSON + CSV

Example pipeline

The ground truth dataset and baseline results are generated in different steps
for each parameter:

1. The provided CSV files and SQL schema are loaded into a MySQL relational database.
2. Mappings are executed by accessing the MySQL relational database to construct a knowledge graph in N-Triples as RDF format

The pipeline is executed 5 times from which the median execution time of each
step is calculated and reported. Each step with the median execution time is
then reported in the baseline results with all its measured metrics.
Knowledge graph construction timeout is set to 24 hours. 
The execution is performed with the following tool: https://github.com/kg-construct/challenge-tool,
you can adapt the execution plans for this example pipeline to your own needs.

Each parameter has its own directory in the ground truth dataset with the
following files:

• Input dataset as CSV.
• Mapping file as RML.
• Execution plan for the pipeline in metadata.json.

Datasets

Knowledge Graph Construction Parameters

The dataset consists of:

• Input dataset as CSV for each parameter.
• Mapping file as RML for each parameter.
• Baseline results for each parameter with the example pipeline.
• Ground truth dataset for each parameter generated with the example pipeline.

Format

All input datasets are provided as CSV, depending on the parameter that is being
evaluated, the number of rows and columns may differ. The first row is always
the header of the CSV.

GTFS-Madrid-Bench

The dataset consists of:

• Input dataset as CSV with SQL schema for the scaling and a combination of XML,
• CSV, and JSON is provided for the heterogeneity.
• Mapping file as RML for both scaling and heterogeneity.
• SPARQL queries to retrieve the results.
• Baseline results with the example pipeline.
• Ground truth dataset generated with the example pipeline.

Format

CSV datasets always have a header as their first row.
JSON and XML datasets have their own schema.

Evaluation criteria

Submissions must evaluate the following metrics:

• 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.
• 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.
• 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.

Expected output

Duplicate values

Empty values

Mappings

Properties

Records

Joins

1-1 joins

1-N joins

1-N joins

N-M joins

GTFS Madrid Bench

Generated Knowledge Graph

Queries