We are very pleased to announce that our group got a paper accepted for presentation at KEOD21 (International Conference on Knowledge Engineering and Ontology Development). KEOD aims at becoming a major meeting point for researchers and practitioners interested in the study and development of methodologies and technologies for Knowledge Engineering and Ontology Development.
Here is the abstract and the link to the paper:
A Scalable Approach for Distributed Reasoning over Large-scale OWL Datasets
By
Heba Mohamed,
Said Fathalla,
Jens Lehmann, and
Hajira Jabeen.
Abstract
With the tremendous increase in the volume of semantic data on the Web, reasoning over such an amount of data has become a challenging task. On the other hand, the traditional centralized approaches are no longer feasible for large-scale data due to the limitations of software and hardware resources. Therefore, horizontal scalability is desirable. We develop a scalable distributed approach for RDFS and OWL Horst Reasoning over large-scale OWL datasets. The eminent feature of our approach is that it combines an optimized execution strategy, pre-shuffling method, and duplication elimination strategy, thus achieving an efficient distributed reasoning mechanism. We implemented our approach as open-source in Apache Spark using Resilient Distributed Datasets (RDD) as a parallel programming model. As a use case, our approach is used by the SANSA framework for large-scale semantic reasoning over OWL datasets. The evaluation results have shown the strength of the proposed approach for both data and node scalability.
