OWLStats: Distributed computation of OWL dataset statistics

Nowadays, ontologies are used in various application areas, involving Artificial Intelligence, Natural Language Processing, Data Integration, and Knowledge Management. It is essential to know the internal structure, distribution, and coherence of the published datasets to make it easier to reuse, interlink, integrate, infer, or query. Therefore, there is a pressing need to obtain a clear view of OWL datasets became more prevalent. In this paper, we present OWLStats, a software component for computing statistical information about large scale OWL datasets in a distributed manner. We present the primary distributed in-memory approach for computing 32 different statistical criteria for OWL datasets utilizing Apache Spark, which can scale horizontally to a cluster of machines. OWLStats has been integrated into the SANSA framework. The preliminary results prove that OWLStats is linearly scalable in terms of data scalability.