- From: Gunjan Singh <gunjans@iiitd.ac.in>
- Date: Wed, 27 Jul 2022 14:05:38 +0530
- To: undisclosed-recipients:;
- Message-ID: <CADcark=_=LB42dh-vCe4gtuO+kO+LLddTTVGsQGvcLTVq2tK1Q@mail.gmail.com>
Dear All, TL;DR ************************ This is the 2nd call for participation for the second edition of the Semantic Reasoning Evaluation Challenge (SemREC) <https://semrec.github.io/> collocated with the 21st International Semantic Web Conference <https://iswc2022.semanticweb.org/>. We invite you to make submissions in the following categories - 1. *Ontologies* - An ontology developed for a real-world application but proved to be a challenge for the existing reasoners. 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. 2. *Systems* 1. 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). 2. SPARQL query engines (new this year) 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. Deadline - 15 August 2022, 23:59 (AOE) If you have any questions, please contact me (gunjans@iiitd.ac.in). Further details are on the website: https://semrec.github.io/. ************************ Longer version ************************* 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 also 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 the ontology developers to not build 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 second edition of this challenge includes the following tasks- - Task-1 - Ontologies. 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. - Task-2 - Systems - Ontology/RDFS Reasoners. Submit an ontology/RDFS reasoner that uses neural-symbolic techniques for reasoning and optimization. In terms of technique used, the submissions could fall under any of the below (or related) categories. 1. Using learning-based techniques for performance optimization of traditional reasoning algorithms. 2. Inductive reasoning techniques based on a subsymbolic representation of entities and relations learned through the maximization of an objective function over valid triples. 3. Techniques that can learn the deductive reasoning aspect using the ontology axioms. 4. Neural Multi-hop reasoners to deal with reasoning where multi-hop inference is required. Based on precision and recall, 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). 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. 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, and 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. - For Task 2, we expect a detailed description of the system, including evaluating the system on the provided datasets. We will further evaluate the submitted systems on a subset of the test datasets for scalability (performance evaluation on large and expressive ontologies) and transfer capabilities (ability to reason over ontologies from different domains). 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 (overleaf template) <https://www.overleaf.com/latex/templates/ceurart-template-for-submissions-to-semrec-challenge/qktzxsbyhsdp>. The proceedings will be published as a volume of CEUR-WS <http://ceur-ws.org/>. Submissions should be made in the form of a pdf document on EasyChair. Website: https://semrec.github.io/ Organizers Gunjan Singh, IIIT-Delhi, India. Raghava Mutharaju, IIIT-Delhi, India. Pavan Kapanipathi, IBM T.J. Watson Research Center, USA. Best regards, Gunjan -- Thanks and Regards Gunjan Singh Ph. D Student, KRaCR Lab <https://kracr.iiitd.edu.in/>, IIIT-Delhi <https://cse.iiitd.ac.in/>, India IBM Research Fellow Homepage - https://gunjansingh1.github.io/
Received on Wednesday, 27 July 2022 08:36:02 UTC