Knowledge Graph Construction has seen a wide uptake among academics and industry. Previous editions of Knowledge Graph Construction Workshop have focused on either benchmarking the performance of knowledge graph construction implementations or the conformance of the implementations according to the latest RML modules. For this year, the W3C Community Group on Knowledge Graph Construction introduces 3 challenges, which aim to cover the three dimensions of knowledge graph construction of heterogeneous data; i) performance, ii) conformance, and iii) mapping methodology.
Workflow for submissions of the Challenge:
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At least one author of each tool needs to present the results during the workshop
(virtual presentations are not allowed)
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 sparking interest among RDF graph construction systems to comply with the new RML specifications and its modules while benchmarking them regarding e.g. execution time, CPU, memory usage, or a combination of these metrics.
The task is to comply with the new RML specification and its modules in this challenge while also aiming at an efficient implementation regarding execution time and computing resources e.g. CPU and memory usage. This challenge is not limited to execution times to create the fastest pipeline, but also computing resources to achieve the most efficient pipeline. We will provide the same machine for each participant to run the evaluation.
The W3C Community Group on Knowledge Graph Construction has prepared a conformance challenge with the new RDF Mapping Language (RML) modules. New modules such as RML IO Registry and RML Logical Views were introduced as well as new test cases with improved specifications.
The task is to comply with the new RML specification and its modules in this challenge.
Test compliance of an engine with all new RML modules:
Although RML has become the de facto standard for constructing knowledge graphs from heterogeneous data sources, the design space for defining and executing mappings is far from closed. There remains significant potential to explore alternative approaches to generating knoweldge graph from heterogeneous data. This challenge track invites participants to push beyond existing approaches and propose novel solutions for knowledge graph generation from heterogeneous data. Participants may build upon RML and its ecosystem, introduce extensions or optimizations, or depart from RML entirely in favor of new mapping models, languages, automation or processing techniques. The focus is on innovation in how mappings are defined, and executed, as well as on demonstrating practical benefits such as (re-)usability, and expressiveness. By encouraging a broad range of approaches, this challenge aims to foster comparative insights into alternative techniques for knowledge graph construction from heterogeneous data.
For this task, we will provide a set of input datasets in different formats, along with either (i) an ontology the output must comply with, or (ii) a target RDF graph to be generated from the input datasets. Participants may use any approach, as long as it produces the required RDF graph.