EMSR: An Efficient Method of Streaming Reasoning

Recently the topic of how to improve the efficiency of semantic reasoning on large-scale knowledge graph has gained considerable attention from global researchers and engineers. Most of existing distributed parallel algorithms for inference based on OWL Horst ruleset require multiple iterations. Moreover, in the process of which, the data stored repeatedly generate redundant records, resulting the reasoning in low overall efficiency. In order to address the challenges, firstly, we presents a storage solution combining variable storage and multivariable connector in accordance with characteristics of OWL Horst ruleset in the context of knowledge graph, aiming at reduction of repeated data storage and data transmission cost. Then, on the basis of such scheme, a streaming reasoning algorithm is introduced to curtail iterations and promote efficiency. Experimental results on LUBM and DBpedia datasets demonstrate that our proposed framework and algorithm could deliver superior performance in scalability and efficiency.