Open AccessSWRL Parallel Reasoning Implementation with Spark SQL
Author(s) -
Wan Li,
Huaai Kang,
Dongbo Ma,
Weiwei Wei
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/719/1/012020
Subject(s) - computer science , spark (programming language) , sql , scalability , ontology , programming language , semantic web , semantic web rule language , big data , database , software engineering , information retrieval , semantic web stack , semantic analytics , data mining , philosophy , epistemology
With the rapid development of semantic Web and big data technology, ontology data has the characteristics of large-scale, high-speed growth and diversity which big data has. On one hand, the conventional ontology reasoners do not scale well for large amounts of ontologies because they are designed for run on a single machine. On the other hand, the existing scalable reasoners are not perfect enough, for example, to completely support the widely used Semantic Web Rule Language (SWRL) rules. This paper presents an implementation for SWRL scalable parallel reasoning using the Spark SQL programming model, and optimizes and processes some of the problems in the implementation.