Semantic Reasoning Evaluation Challenge (SemREC)

20th International Semantic Web Conference (ISWC 2021)

Challenge Description

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 still can not deal with very expressive ontology languages. 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 the ontology developers to not build ontologies that vary in terms of size and expressivity is the performance bottleneck of the reasoners. This challenge includes three tasks that aim to deal with this chicken and egg problem.

  • Task-1 - Submit a real-world ontology that is a challenge in terms of the reasoning time or memory consumed during reasoning. We will be evaluating the submitted ontologies based on the time and the memory consumed for a reasoning task, such as classification.

  • Task-2 - Submit a description logic reasoner that makes use of traditional techniques such as tableau algorithms and saturation rules. We will evaluate the performance and the scalability of the submitted systems on the datasets based on the time taken and memory consumed on the ontology classification task. This will provide an insight into the progress in the development of reasoners since the last reasoner evaluation challenge (ORE 2015).

  • Task-3 - Submit an ontology/RDFS reasoner that makes use of neuro-symbolic techniques for reasoning and optimization. We will be evaluating two types of neuro-symbolic systems: (a) that approximate the entailment reasoning for addressing the time complexity problem, or (b) predicting missing and plausible axioms for completion. We will evaluate the submitted systems on the test datasets based on the time taken, memory consumed, precision and recall. (Please check the references for some samples of the work that fall in this category).

This challenge will be collocated with the 20th International Semantic Web Conference.

We have a discussion group for the challenge where we share the latest news with the participants and discuss issues related to the evaluation rounds.

Submission Details

Participants are requested to make a manuscript submission describing their entry. For Task 1, 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 of a few axioms, redesigning the ontology, etc.), and the (potential) applications in which the ontology could be used. For Tasks 2 and 3, we expect a detailed description of the system, including evaluating the system on the provided datasets. The submissions can be either in the form of short papers of length 5 pages or long papers of length 10-12 pages. All the submissions must be in English and follow the 1-column CEUR-ART style (link to the overleaf page would be updated soon). The proceedings will be published as a volume of CEUR-WS. Submissions should be made in the form of a pdf document on EasyChair (link will be updated soon).



Biswesh Mohapatra

IIIT-Bangalore, India


  1. Sutapa Mondal, Sumit Bhatia, Raghava Mutharaju. EmEL++: Embeddings for EL++ Description Logic. Spring Symposium on Combining Machine Learning and Knowledge Engineering (AAAI-MAKE), 2021.
  2. Gunjan Singh, Sutapa Mondal, Sumit Bhatia, Raghava Mutharaju. Neuro-Symbolic Techniques for Description Logic Reasoning. Student Abstract, Association for the Advancement of Artificial Intelligence (AAAI), 2020.
  3. Monireh Ebrahimi, Aaron Eberhart, Federico Bianchi, Pascal Hitzler. Towards bridging the neuro-symbolic gap: deep deductive reasoners . Applied Intelligence, 2021.
  4. Claudia dAmato, Andrea G. B. Tettamanzi, Tran Duc Minh. Evolutionary Discovery of Multi-relational Association Rules from Ontological Knowledge Bases . European Knowledge Acquisition Workshop (EKAW), 2016.