Reasoning with Large Scale OWL 2 EL Ontologies Based on MapReduce

Abstract

OWL 2 EL, which is underpinned by the description logic $\mathcal {EL}$, has been used to build terminological ontologies in real applications, like biomedicine, multimedia and transportation. On the other hand, there have been techniques that allow developers and users acquiring large scale ontologies by automatically extracting data from different sources or integrating different domain ontologies. Thus the issue of handling large scale ontologies has to be tackled. In this short paper, we report our work on classification of OWL 2 EL ontologies using MapReduce, which is a distributed computing model for data processing. We discuss the main problems when we use MapReduce to handle OWL 2 EL classification and how we address these problems. We implement the algorithm using Hadoop, and evaluate it on a cluster of machines. The experimental results show that our prototype system achieves a linear scalability on large scale ontologies.