Parallel reasoning algorithm for streaming rdf data

Abstract

The invention provides a parallel reasoning algorithm for streaming RDF data, which comprises the steps of building a pseudo bidirectional network of a rule, and building an intermediate node if a class link variable exists in rule nodes; acquiring a batch of new data in a streaming data flow and data generated by previous reasoning at regular time to act as input data, performing classification on the input data or newly building corresponding nodes, and storing the nodes into corresponding Redis clusters; judging whether antecedents monitored by the corresponding intermediate nodes or rule nodes are all satisfied or not by combining the pseudo bidirectional network for the inputted triple data, thus performing reasoning on the rule, and generating reasoning data; and realizing parallel streaming reasoning for an OWL Horst rule of the RDF data integrally and efficiently through deleting repeated reasoning data in real time and storing all data generated by the current time of reasoning into the Redis clusters to act as input data of the next time of reasoning.

Description

technical field [0001] The invention belongs to the technical field of semantic web, and specifically relates to a parallel reasoning algorithm for streaming RDF data. Background technique [0002] In recent years, researchers have gradually realized the importance of research on parallel reasoning algorithms for real-time streaming data, but there are still few related algorithms proposed in this field, and further research is needed. At the same time, there are quite a lot of researches on reasoning in intelligent technology, such as knowledge discovery and case reasoning. It has become a consensus in academia and industry to solve large-scale RDF streaming data related problems through distributed parallel computing. [0003] Research on RDFS / OWL streaming parallel reasoning is a relatively new field at present. Barbieri D F et al. proposed an incremental reasoning algorithm based on streaming and rich background knowledge. This algorithm adds expiration time informatio...

AI Technical Summary

Problems solved by technology

However, since Slider is only designed for RDFS rules, it is not suitable for complex OWL Horst rule reasoning

[0004] Today's challenges in large-scale RDF file reasoning are: distributed data on the network is difficult to obtain appropriate triples; the growing amount of data requires scalable computing power for large data sets; existing reasoning methods are Designed for static ontologies where data is usually changing in the real world

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