Despite the development of several ontology reasoning optimizations, the traditional methods either do not scale well or only cover a subset of OWL 2 language constructs. As an alternative, neuro-symbolic approaches are gaining significant attention. However, the existing methods can not deal with very expressive ontology languages. Other than that, some SPARQL query engines also support reasoning, but their performance is still limited. To find and improve these performance bottlenecks of the reasoners, we ideally need several real-world ontologies that span the broad spectrum in terms of their size and expressivity. However, that is often not the case. One of the potential reasons for ontology developers not building ontologies that vary in terms of size and expressivity is the performance bottleneck of the reasoners. SemREC aims to deal with this chicken and egg problem.
The third edition of this challenge includes the following tasks.
Submit a real-world ontology that is a challenge in terms of the reasoning time or memory consumed during reasoning. We expect a detailed description of the ontology along with the analysis of the reasoning performance, the workarounds if any, that were used to make the ontology less challenging (for example, dropping a few axioms, redesigning the ontology, etc.), and the (potential) applications in which the ontology could be used. We will be evaluating the submitted ontologies based on the time consumed for a reasoning task, such as classification, and the memory consumed during reasoning.
Ontology/RDFS Reasoners. Submit an ontology/RDFS reasoner that uses neural-symbolic techniques for reasoning and optimization. We will evaluate the submitted systems on the test datasets for scalability (performance evaluation on large and expressive ontologies) and transfer capabilities (ability to reason over ontologies from different domains) based on precision and recall. We expect a detailed description of the system, including an evaluation of the system on the provided datasets.
SPARQL query engines that support entailment regimes such as RDF, RDFS, or OWL 2. We expect a detailed description of the system, including an evaluation of the system on the provided datasets.
The organizers: Gunjan Singh (Ph. D student, KRaCR Lab, IIIT Delhi), Raghava Mutharaju (Assistant Professor, KRaCR Lab, IIIT Delhi), Pavan Kapanipathi ( IBM T.J. Watson Research Center, USA)